From bec719e809b5ff5be3706c7c851437273e56c332 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Sun, 20 Aug 2023 20:48:30 -0700 Subject: [PATCH 01/18] V2.1rc (#4) * change to README * update README * frozen versions for v2 * frozen requirements, ISLP==0.3.19 * v2.1 notebooks excluding 10,13 * v2.1 of Ch13 * pairing notebooks * v2.1 of Ch10 * update README for v2.1 * update README * remove workflows: notebooks can be tested in ISLP --- .github/workflows/test_Ch02.yml | 40 - .github/workflows/test_Ch03.yml | 40 - .github/workflows/test_Ch04.yml | 40 - .github/workflows/test_Ch05.yml | 40 - .github/workflows/test_Ch06.yml | 40 - .github/workflows/test_Ch07.yml | 40 - .github/workflows/test_Ch08.yml | 40 - .github/workflows/test_Ch09.yml | 40 - .github/workflows/test_Ch10.yml | 40 - .github/workflows/test_Ch11.yml | 40 - .github/workflows/test_Ch12.yml | 40 - .github/workflows/test_Ch13.yml | 40 - Ch02-statlearn-lab.ipynb | 1472 +++++++++++++++---------------- Ch03-linreg-lab.Rmd | 6 +- Ch03-linreg-lab.ipynb | 469 +++++----- Ch04-classification-lab.Rmd | 2 +- Ch04-classification-lab.ipynb | 1110 +++++++++++------------ Ch05-resample-lab.Rmd | 4 +- Ch05-resample-lab.ipynb | 326 +++---- Ch06-varselect-lab.Rmd | 4 +- Ch06-varselect-lab.ipynb | 704 +++++++-------- Ch07-nonlin-lab.Rmd | 3 +- Ch07-nonlin-lab.ipynb | 599 +++++++------ Ch08-baggboost-lab.ipynb | 450 +++++----- Ch09-svm-lab.ipynb | 418 ++++----- Ch10-deeplearning-lab.Rmd | 19 +- Ch10-deeplearning-lab.ipynb | 827 +++++++++-------- Ch11-surv-lab.ipynb | 420 ++++----- Ch12-unsup-lab.Rmd | 4 +- Ch12-unsup-lab.ipynb | 708 +++++++-------- Ch13-multiple-lab.Rmd | 5 +- Ch13-multiple-lab.ipynb | 388 ++++---- README.md | 8 +- requirements.txt | 33 +- 34 files changed, 3978 insertions(+), 4481 deletions(-) delete mode 100644 .github/workflows/test_Ch02.yml delete mode 100644 .github/workflows/test_Ch03.yml delete mode 100644 .github/workflows/test_Ch04.yml delete mode 100644 .github/workflows/test_Ch05.yml delete mode 100644 .github/workflows/test_Ch06.yml delete mode 100644 .github/workflows/test_Ch07.yml delete mode 100644 .github/workflows/test_Ch08.yml delete mode 100644 .github/workflows/test_Ch09.yml delete mode 100644 .github/workflows/test_Ch10.yml delete mode 100644 .github/workflows/test_Ch11.yml delete mode 100644 .github/workflows/test_Ch12.yml delete mode 100644 .github/workflows/test_Ch13.yml diff --git a/.github/workflows/test_Ch02.yml b/.github/workflows/test_Ch02.yml deleted file mode 100644 index dfcce44..0000000 --- a/.github/workflows/test_Ch02.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch02 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch02 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch02-statlearn-lab.ipynb --allow-errors - - diff --git a/.github/workflows/test_Ch03.yml b/.github/workflows/test_Ch03.yml deleted file mode 100644 index d4022ce..0000000 --- a/.github/workflows/test_Ch03.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch03 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch03 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch03*ipynb - - diff --git a/.github/workflows/test_Ch04.yml b/.github/workflows/test_Ch04.yml deleted file mode 100644 index b148d82..0000000 --- a/.github/workflows/test_Ch04.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch04 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch04 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch04*ipynb - - diff --git a/.github/workflows/test_Ch05.yml b/.github/workflows/test_Ch05.yml deleted file mode 100644 index 2b01551..0000000 --- a/.github/workflows/test_Ch05.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch05 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch05 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch05*ipynb - - diff --git a/.github/workflows/test_Ch06.yml b/.github/workflows/test_Ch06.yml deleted file mode 100644 index 3ed430c..0000000 --- a/.github/workflows/test_Ch06.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch06 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch06 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch06*ipynb - - diff --git a/.github/workflows/test_Ch07.yml b/.github/workflows/test_Ch07.yml deleted file mode 100644 index d87d848..0000000 --- a/.github/workflows/test_Ch07.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch07 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch07 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch07*ipynb - - diff --git a/.github/workflows/test_Ch08.yml b/.github/workflows/test_Ch08.yml deleted file mode 100644 index 2b7e77b..0000000 --- a/.github/workflows/test_Ch08.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch08 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch08 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch08*ipynb - - diff --git a/.github/workflows/test_Ch09.yml b/.github/workflows/test_Ch09.yml deleted file mode 100644 index e88a6ff..0000000 --- a/.github/workflows/test_Ch09.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch09 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch09 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch09*ipynb - - diff --git a/.github/workflows/test_Ch10.yml b/.github/workflows/test_Ch10.yml deleted file mode 100644 index ce97c12..0000000 --- a/.github/workflows/test_Ch10.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch10 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch10 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch10*ipynb - - diff --git a/.github/workflows/test_Ch11.yml b/.github/workflows/test_Ch11.yml deleted file mode 100644 index a2a4ee2..0000000 --- a/.github/workflows/test_Ch11.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch11 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch11 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch11*ipynb - - diff --git a/.github/workflows/test_Ch12.yml b/.github/workflows/test_Ch12.yml deleted file mode 100644 index f231523..0000000 --- a/.github/workflows/test_Ch12.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch12 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch12 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch12*ipynb - - diff --git a/.github/workflows/test_Ch13.yml b/.github/workflows/test_Ch13.yml deleted file mode 100644 index 77ff83d..0000000 --- a/.github/workflows/test_Ch13.yml +++ /dev/null @@ -1,40 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: Building Ch13 notebook - -# Controls when the workflow will run -on: - workflow_dispatch: - environment: - description: 'Environment to run tests against' - type: environment - required: true - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v3 - - uses: actions/setup-python@v2 - with: - python-version: '3.10' - cache: 'pip' - - # Install - - name: Install dependencies - run: | - pip install -r requirements.txt - pip install ISLP jupyter - - # Runs a set of commands using the runners shell - - name: Build notebook for Ch13 (some errors, not really a test) - run: | - jupyter nbconvert --execute --inplace Ch13*ipynb - - diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb index a848367..f137f1c 100644 --- a/Ch02-statlearn-lab.ipynb +++ b/Ch02-statlearn-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4a4000ea", + "id": "245f0c86", "metadata": {}, "source": [ "\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "ca503bf1", + "id": "5ab29948", "metadata": {}, "source": [ "## Getting Started" @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "4d90d49a", + "id": "ed622870", "metadata": {}, "source": [ "To run the labs in this book, you will need two things:\n", @@ -33,7 +33,7 @@ }, { "cell_type": "markdown", - "id": "c05e9a74", + "id": "844d37fc", "metadata": {}, "source": [ "You can download and install `Python3` by following the instructions available at [anaconda.com](http://anaconda.com). " @@ -41,7 +41,7 @@ }, { "cell_type": "markdown", - "id": "a4fa6a12", + "id": "462ff1fe", "metadata": {}, "source": [ " There are a number of ways to get access to `Jupyter`. Here are just a few:\n", @@ -63,7 +63,7 @@ }, { "cell_type": "markdown", - "id": "fb245ea1", + "id": "b46f9182", "metadata": {}, "source": [ "## Basic Commands\n" @@ -71,7 +71,7 @@ }, { "cell_type": "markdown", - "id": "df72fe8e", + "id": "54060fd9", "metadata": {}, "source": [ "In this lab, we will introduce some simple `Python` commands. \n", @@ -83,7 +83,7 @@ }, { "cell_type": "markdown", - "id": "713233e6", + "id": "d3dbd0e9", "metadata": {}, "source": [ "Like most programming languages, `Python` uses *functions*\n", @@ -99,13 +99,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "3e6aae64", + "id": "9e8aa21f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.279506Z", - "iopub.status.busy": "2023-08-15T18:48:57.279285Z", - "iopub.status.idle": "2023-08-15T18:48:57.289471Z", - "shell.execute_reply": "2023-08-15T18:48:57.289054Z" + "iopub.execute_input": "2023-08-21T03:36:04.654555Z", + "iopub.status.busy": "2023-08-21T03:36:04.654242Z", + "iopub.status.idle": "2023-08-21T03:36:04.664431Z", + "shell.execute_reply": "2023-08-21T03:36:04.663990Z" } }, "outputs": [ @@ -123,7 +123,7 @@ }, { "cell_type": "markdown", - "id": "790e89ef", + "id": "27d935f8", "metadata": {}, "source": [ " The following command will provide information about the `print()` function." @@ -132,13 +132,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "086066fd", + "id": "d62ec119", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.292564Z", - "iopub.status.busy": "2023-08-15T18:48:57.292381Z", - "iopub.status.idle": "2023-08-15T18:48:57.319543Z", - "shell.execute_reply": "2023-08-15T18:48:57.319262Z" + "iopub.execute_input": "2023-08-21T03:36:04.666908Z", + "iopub.status.busy": "2023-08-21T03:36:04.666727Z", + "iopub.status.idle": "2023-08-21T03:36:04.693658Z", + "shell.execute_reply": "2023-08-21T03:36:04.693389Z" } }, "outputs": [], @@ -148,7 +148,7 @@ }, { "cell_type": "markdown", - "id": "1519b474", + "id": "04b3e2a3", "metadata": {}, "source": [ "Adding two integers in `Python` is pretty intuitive." @@ -157,13 +157,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "cb86e827", + "id": "c64e9f4d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.321132Z", - "iopub.status.busy": "2023-08-15T18:48:57.321031Z", - "iopub.status.idle": "2023-08-15T18:48:57.324040Z", - "shell.execute_reply": "2023-08-15T18:48:57.323786Z" + "iopub.execute_input": "2023-08-21T03:36:04.695168Z", + "iopub.status.busy": "2023-08-21T03:36:04.695071Z", + "iopub.status.idle": "2023-08-21T03:36:04.697996Z", + "shell.execute_reply": "2023-08-21T03:36:04.697736Z" } }, "outputs": [ @@ -184,7 +184,7 @@ }, { "cell_type": "markdown", - "id": "995ac4a8", + "id": "cd754cba", "metadata": {}, "source": [ "In `Python`, textual data is handled using\n", @@ -197,13 +197,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "f4d45d80", + "id": "9abccc1f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.325425Z", - "iopub.status.busy": "2023-08-15T18:48:57.325349Z", - "iopub.status.idle": "2023-08-15T18:48:57.327462Z", - "shell.execute_reply": "2023-08-15T18:48:57.327221Z" + "iopub.execute_input": "2023-08-21T03:36:04.699502Z", + "iopub.status.busy": "2023-08-21T03:36:04.699420Z", + "iopub.status.idle": "2023-08-21T03:36:04.701419Z", + "shell.execute_reply": "2023-08-21T03:36:04.701183Z" } }, "outputs": [ @@ -224,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "54a86535", + "id": "c28db903", "metadata": {}, "source": [ " A string is actually a type of *sequence*: this is a generic term for an ordered list. \n", @@ -234,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "f2cd64af", + "id": "5fdcc5a1", "metadata": {}, "source": [ "The following command instructs `Python` to join together\n", @@ -246,13 +246,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "e386aff9", + "id": "802ca33c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.328950Z", - "iopub.status.busy": "2023-08-15T18:48:57.328870Z", - "iopub.status.idle": "2023-08-15T18:48:57.331048Z", - "shell.execute_reply": "2023-08-15T18:48:57.330802Z" + "iopub.execute_input": "2023-08-21T03:36:04.702877Z", + "iopub.status.busy": "2023-08-21T03:36:04.702786Z", + "iopub.status.idle": "2023-08-21T03:36:04.704849Z", + "shell.execute_reply": "2023-08-21T03:36:04.704596Z" } }, "outputs": [ @@ -274,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "185fdcfa", + "id": "5492ecd1", "metadata": {}, "source": [ "Note that we used the brackets\n", @@ -287,13 +287,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "f81b1212", + "id": "a8c72744", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.332609Z", - "iopub.status.busy": "2023-08-15T18:48:57.332515Z", - "iopub.status.idle": "2023-08-15T18:48:57.334649Z", - "shell.execute_reply": "2023-08-15T18:48:57.334387Z" + "iopub.execute_input": "2023-08-21T03:36:04.706312Z", + "iopub.status.busy": "2023-08-21T03:36:04.706207Z", + "iopub.status.idle": "2023-08-21T03:36:04.708359Z", + "shell.execute_reply": "2023-08-21T03:36:04.708116Z" } }, "outputs": [ @@ -315,7 +315,7 @@ }, { "cell_type": "markdown", - "id": "f421a679", + "id": "8f42ea1d", "metadata": {}, "source": [ "The result may appear slightly counterintuitive: why did `Python` not add the entries of the lists\n", @@ -327,7 +327,7 @@ }, { "cell_type": "markdown", - "id": "911db9e2", + "id": "69015df5", "metadata": {}, "source": [ "This example reflects the fact that \n", @@ -340,7 +340,7 @@ }, { "cell_type": "markdown", - "id": "2045f04d", + "id": "16bfc4a2", "metadata": {}, "source": [ "## Introduction to Numerical Python\n", @@ -352,7 +352,7 @@ }, { "cell_type": "markdown", - "id": "b4ef08ea", + "id": "f5bed3f0", "metadata": {}, "source": [ " To access `numpy`, we must first `import` it." @@ -361,13 +361,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "13739a14", + "id": "f1c7d1db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.336173Z", - "iopub.status.busy": "2023-08-15T18:48:57.336085Z", - "iopub.status.idle": "2023-08-15T18:48:57.487183Z", - "shell.execute_reply": "2023-08-15T18:48:57.486459Z" + "iopub.execute_input": "2023-08-21T03:36:04.709873Z", + "iopub.status.busy": "2023-08-21T03:36:04.709781Z", + "iopub.status.idle": "2023-08-21T03:36:04.767425Z", + "shell.execute_reply": "2023-08-21T03:36:04.766663Z" }, "lines_to_next_cell": 0 }, @@ -378,7 +378,7 @@ }, { "cell_type": "markdown", - "id": "587207d9", + "id": "5c8614e7", "metadata": {}, "source": [ "In the previous line, we named the `numpy` *module* `np`; an abbreviation for easier referencing." @@ -386,7 +386,7 @@ }, { "cell_type": "markdown", - "id": "c485aeb9", + "id": "ba1224a6", "metadata": {}, "source": [ "In `numpy`, an *array* is a generic term for a multidimensional\n", @@ -397,13 +397,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "f3f62d0a", + "id": "e2ea2bfd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.492222Z", - "iopub.status.busy": "2023-08-15T18:48:57.491645Z", - "iopub.status.idle": "2023-08-15T18:48:57.497340Z", - "shell.execute_reply": "2023-08-15T18:48:57.496428Z" + "iopub.execute_input": "2023-08-21T03:36:04.772015Z", + "iopub.status.busy": "2023-08-21T03:36:04.771631Z", + "iopub.status.idle": "2023-08-21T03:36:04.777911Z", + "shell.execute_reply": "2023-08-21T03:36:04.777302Z" }, "lines_to_next_cell": 0 }, @@ -415,7 +415,7 @@ }, { "cell_type": "markdown", - "id": "d5e7e5e3", + "id": "a977e05a", "metadata": {}, "source": [ "Note that if you forgot to run the `import numpy as np` command earlier, then\n", @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "5ba313c6", + "id": "742431b6", "metadata": {}, "source": [ "Since `x` and `y` have been defined using `np.array()`, we get a sensible result when we add them together. Compare this to our results in the previous section,\n", @@ -436,13 +436,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "3cff111f", + "id": "59fbf9fd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.501566Z", - "iopub.status.busy": "2023-08-15T18:48:57.501206Z", - "iopub.status.idle": "2023-08-15T18:48:57.515553Z", - "shell.execute_reply": "2023-08-15T18:48:57.514659Z" + "iopub.execute_input": "2023-08-21T03:36:04.781791Z", + "iopub.status.busy": "2023-08-21T03:36:04.781430Z", + "iopub.status.idle": "2023-08-21T03:36:04.788256Z", + "shell.execute_reply": "2023-08-21T03:36:04.787586Z" }, "lines_to_next_cell": 0 }, @@ -464,7 +464,7 @@ }, { "cell_type": "markdown", - "id": "96db430f", + "id": "2ceccc2b", "metadata": {}, "source": [ " \n", @@ -473,7 +473,7 @@ }, { "cell_type": "markdown", - "id": "801647e7", + "id": "74be6d74", "metadata": {}, "source": [ "In `numpy`, matrices are typically represented as two-dimensional arrays, and vectors as one-dimensional arrays. {While it is also possible to create matrices using `np.matrix()`, we will use `np.array()` throughout the labs in this book.}\n", @@ -483,13 +483,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "30d6c7f8", + "id": "2279437e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.520927Z", - "iopub.status.busy": "2023-08-15T18:48:57.520636Z", - "iopub.status.idle": "2023-08-15T18:48:57.528716Z", - "shell.execute_reply": "2023-08-15T18:48:57.528048Z" + "iopub.execute_input": "2023-08-21T03:36:04.792995Z", + "iopub.status.busy": "2023-08-21T03:36:04.792549Z", + "iopub.status.idle": "2023-08-21T03:36:04.800387Z", + "shell.execute_reply": "2023-08-21T03:36:04.799738Z" }, "lines_to_next_cell": 0 }, @@ -513,7 +513,7 @@ }, { "cell_type": "markdown", - "id": "5df2b6c3", + "id": "f96f304d", "metadata": {}, "source": [ " \n", @@ -522,7 +522,7 @@ }, { "cell_type": "markdown", - "id": "d7804f5e", + "id": "f764f7d1", "metadata": {}, "source": [ "The object `x` has several \n", @@ -534,13 +534,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "42d49be4", + "id": "75bf1b1e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.532634Z", - "iopub.status.busy": "2023-08-15T18:48:57.532261Z", - "iopub.status.idle": "2023-08-15T18:48:57.543935Z", - "shell.execute_reply": "2023-08-15T18:48:57.543179Z" + "iopub.execute_input": "2023-08-21T03:36:04.804503Z", + "iopub.status.busy": "2023-08-21T03:36:04.804231Z", + "iopub.status.idle": "2023-08-21T03:36:04.810136Z", + "shell.execute_reply": "2023-08-21T03:36:04.809353Z" } }, "outputs": [ @@ -561,7 +561,7 @@ }, { "cell_type": "markdown", - "id": "765e1496", + "id": "4e3b83bf", "metadata": {}, "source": [ "The output indicates that `x` is a two-dimensional array. \n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "de32b275", + "id": "58292240", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.549690Z", - "iopub.status.busy": "2023-08-15T18:48:57.549447Z", - "iopub.status.idle": "2023-08-15T18:48:57.554487Z", - "shell.execute_reply": "2023-08-15T18:48:57.553790Z" + "iopub.execute_input": "2023-08-21T03:36:04.813981Z", + "iopub.status.busy": "2023-08-21T03:36:04.813417Z", + "iopub.status.idle": "2023-08-21T03:36:04.819511Z", + "shell.execute_reply": "2023-08-21T03:36:04.818936Z" }, "lines_to_next_cell": 0 }, @@ -600,7 +600,7 @@ }, { "cell_type": "markdown", - "id": "044bf011", + "id": "cf9cf94b", "metadata": {}, "source": [ "Why is `x` comprised of integers? This is because we created `x` by passing in exclusively integers to the `np.array()` function.\n", @@ -612,13 +612,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "528c3fa0", + "id": "fc5fff57", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.558246Z", - "iopub.status.busy": "2023-08-15T18:48:57.557892Z", - "iopub.status.idle": "2023-08-15T18:48:57.564135Z", - "shell.execute_reply": "2023-08-15T18:48:57.563353Z" + "iopub.execute_input": "2023-08-21T03:36:04.823519Z", + "iopub.status.busy": "2023-08-21T03:36:04.823222Z", + "iopub.status.idle": "2023-08-21T03:36:04.829861Z", + "shell.execute_reply": "2023-08-21T03:36:04.829223Z" }, "lines_to_next_cell": 2 }, @@ -640,7 +640,7 @@ }, { "cell_type": "markdown", - "id": "053da117", + "id": "41a79641", "metadata": {}, "source": [ "Typing `fun?` will cause `Python` to display \n", @@ -651,13 +651,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "fbf8763b", + "id": "762562a6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.567429Z", - "iopub.status.busy": "2023-08-15T18:48:57.567214Z", - "iopub.status.idle": "2023-08-15T18:48:57.571774Z", - "shell.execute_reply": "2023-08-15T18:48:57.570942Z" + "iopub.execute_input": "2023-08-21T03:36:04.833747Z", + "iopub.status.busy": "2023-08-21T03:36:04.833468Z", + "iopub.status.idle": "2023-08-21T03:36:04.838421Z", + "shell.execute_reply": "2023-08-21T03:36:04.837747Z" }, "lines_to_next_cell": 0 }, @@ -668,7 +668,7 @@ }, { "cell_type": "markdown", - "id": "0df45f3d", + "id": "d4d82167", "metadata": {}, "source": [ "This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`." @@ -677,13 +677,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "482d0c05", + "id": "66d2b82a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.575374Z", - "iopub.status.busy": "2023-08-15T18:48:57.575055Z", - "iopub.status.idle": "2023-08-15T18:48:57.581818Z", - "shell.execute_reply": "2023-08-15T18:48:57.580921Z" + "iopub.execute_input": "2023-08-21T03:36:04.842127Z", + "iopub.status.busy": "2023-08-21T03:36:04.841891Z", + "iopub.status.idle": "2023-08-21T03:36:04.848885Z", + "shell.execute_reply": "2023-08-21T03:36:04.848125Z" }, "lines_to_next_cell": 2 }, @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "757c10a0", + "id": "1e3ba5be", "metadata": {}, "source": [ "The array `x` is two-dimensional. We can find out the number of rows and columns by looking\n", @@ -715,13 +715,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "192cdf26", + "id": "89881402", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.585609Z", - "iopub.status.busy": "2023-08-15T18:48:57.585283Z", - "iopub.status.idle": "2023-08-15T18:48:57.590942Z", - "shell.execute_reply": "2023-08-15T18:48:57.589913Z" + "iopub.execute_input": "2023-08-21T03:36:04.853039Z", + "iopub.status.busy": "2023-08-21T03:36:04.852643Z", + "iopub.status.idle": "2023-08-21T03:36:04.859072Z", + "shell.execute_reply": "2023-08-21T03:36:04.858149Z" }, "lines_to_next_cell": 2 }, @@ -743,7 +743,7 @@ }, { "cell_type": "markdown", - "id": "b3d091f4", + "id": "2967b644", "metadata": {}, "source": [ "A *method* is a function that is associated with an\n", @@ -758,13 +758,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "4420b967", + "id": "0572d3f6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.596192Z", - "iopub.status.busy": "2023-08-15T18:48:57.595890Z", - "iopub.status.idle": "2023-08-15T18:48:57.600791Z", - "shell.execute_reply": "2023-08-15T18:48:57.600018Z" + "iopub.execute_input": "2023-08-21T03:36:04.863011Z", + "iopub.status.busy": "2023-08-21T03:36:04.862610Z", + "iopub.status.idle": "2023-08-21T03:36:04.869722Z", + "shell.execute_reply": "2023-08-21T03:36:04.869078Z" }, "lines_to_next_cell": 0 }, @@ -787,7 +787,7 @@ }, { "cell_type": "markdown", - "id": "ca0ff297", + "id": "e3f49995", "metadata": {}, "source": [ "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function. " @@ -796,13 +796,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "5af5e20f", + "id": "33b10a6f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.605039Z", - "iopub.status.busy": "2023-08-15T18:48:57.604654Z", - "iopub.status.idle": "2023-08-15T18:48:57.611781Z", - "shell.execute_reply": "2023-08-15T18:48:57.609766Z" + "iopub.execute_input": "2023-08-21T03:36:04.874228Z", + "iopub.status.busy": "2023-08-21T03:36:04.873632Z", + "iopub.status.idle": "2023-08-21T03:36:04.879222Z", + "shell.execute_reply": "2023-08-21T03:36:04.878606Z" }, "lines_to_next_cell": 0 }, @@ -825,7 +825,7 @@ }, { "cell_type": "markdown", - "id": "355ad58f", + "id": "2f3dd2c3", "metadata": {}, "source": [ " As another example, the\n", @@ -842,13 +842,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "77a0df31", + "id": "a32716db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.615659Z", - "iopub.status.busy": "2023-08-15T18:48:57.615422Z", - "iopub.status.idle": "2023-08-15T18:48:57.619501Z", - "shell.execute_reply": "2023-08-15T18:48:57.619211Z" + "iopub.execute_input": "2023-08-21T03:36:04.882833Z", + "iopub.status.busy": "2023-08-21T03:36:04.882354Z", + "iopub.status.idle": "2023-08-21T03:36:04.887384Z", + "shell.execute_reply": "2023-08-21T03:36:04.886127Z" } }, "outputs": [ @@ -873,7 +873,7 @@ }, { "cell_type": "markdown", - "id": "e68cddfa", + "id": "2483179e", "metadata": {}, "source": [ "The previous output reveals that `numpy` arrays are specified as a sequence\n", @@ -882,7 +882,7 @@ }, { "cell_type": "markdown", - "id": "4b16c07d", + "id": "e256575f", "metadata": {}, "source": [ "`Python` (and hence `numpy`) uses 0-based\n", @@ -893,13 +893,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "c57ce2ca", + "id": "3db6e1cf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.622217Z", - "iopub.status.busy": "2023-08-15T18:48:57.622128Z", - "iopub.status.idle": "2023-08-15T18:48:57.624279Z", - "shell.execute_reply": "2023-08-15T18:48:57.624023Z" + "iopub.execute_input": "2023-08-21T03:36:04.890861Z", + "iopub.status.busy": "2023-08-21T03:36:04.890532Z", + "iopub.status.idle": "2023-08-21T03:36:04.894815Z", + "shell.execute_reply": "2023-08-21T03:36:04.894149Z" }, "lines_to_next_cell": 0 }, @@ -921,7 +921,7 @@ }, { "cell_type": "markdown", - "id": "9a4feee5", + "id": "0e10119e", "metadata": {}, "source": [ "Similarly, `x_reshape[1,2]` yields the element in the second row and the third column \n", @@ -931,13 +931,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "ea55b034", + "id": "e15c753f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.626107Z", - "iopub.status.busy": "2023-08-15T18:48:57.625991Z", - "iopub.status.idle": "2023-08-15T18:48:57.628267Z", - "shell.execute_reply": "2023-08-15T18:48:57.627959Z" + "iopub.execute_input": "2023-08-21T03:36:04.898049Z", + "iopub.status.busy": "2023-08-21T03:36:04.897796Z", + "iopub.status.idle": "2023-08-21T03:36:04.902137Z", + "shell.execute_reply": "2023-08-21T03:36:04.901538Z" }, "lines_to_next_cell": 0 }, @@ -959,7 +959,7 @@ }, { "cell_type": "markdown", - "id": "7a3c27dd", + "id": "f9c55622", "metadata": {}, "source": [ "Similarly, `x[2]` yields the\n", @@ -972,13 +972,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "b897a965", + "id": "91c6e7d8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.629860Z", - "iopub.status.busy": "2023-08-15T18:48:57.629753Z", - "iopub.status.idle": "2023-08-15T18:48:57.631845Z", - "shell.execute_reply": "2023-08-15T18:48:57.631576Z" + "iopub.execute_input": "2023-08-21T03:36:04.905358Z", + "iopub.status.busy": "2023-08-21T03:36:04.905148Z", + "iopub.status.idle": "2023-08-21T03:36:04.910143Z", + "shell.execute_reply": "2023-08-21T03:36:04.909500Z" } }, "outputs": [ @@ -1009,7 +1009,7 @@ }, { "cell_type": "markdown", - "id": "77ab8534", + "id": "8a840507", "metadata": {}, "source": [ "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory.\n", @@ -1020,7 +1020,7 @@ }, { "cell_type": "markdown", - "id": "8d5214bc", + "id": "ec551f3e", "metadata": {}, "source": [ "We just saw that we can modify an element of an array. Can we also modify a tuple? It turns out that we cannot --- and trying to do so introduces\n", @@ -1030,13 +1030,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "1de2f886", + "id": "59d95dce", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.633340Z", - "iopub.status.busy": "2023-08-15T18:48:57.633238Z", - "iopub.status.idle": "2023-08-15T18:48:57.752298Z", - "shell.execute_reply": "2023-08-15T18:48:57.751937Z" + "iopub.execute_input": "2023-08-21T03:36:04.913411Z", + "iopub.status.busy": "2023-08-21T03:36:04.912985Z", + "iopub.status.idle": "2023-08-21T03:36:05.037046Z", + "shell.execute_reply": "2023-08-21T03:36:05.036755Z" }, "lines_to_next_cell": 2 }, @@ -1060,7 +1060,7 @@ }, { "cell_type": "markdown", - "id": "5f8a5095", + "id": "d594f1af", "metadata": {}, "source": [ "We now briefly mention some attributes of arrays that will come in handy. An array's `shape` attribute contains its dimension; this is always a tuple.\n", @@ -1070,13 +1070,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "634a18d8", + "id": "a6fde9af", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.754084Z", - "iopub.status.busy": "2023-08-15T18:48:57.753976Z", - "iopub.status.idle": "2023-08-15T18:48:57.756236Z", - "shell.execute_reply": "2023-08-15T18:48:57.756009Z" + "iopub.execute_input": "2023-08-21T03:36:05.038645Z", + "iopub.status.busy": "2023-08-21T03:36:05.038536Z", + "iopub.status.idle": "2023-08-21T03:36:05.040823Z", + "shell.execute_reply": "2023-08-21T03:36:05.040589Z" } }, "outputs": [ @@ -1101,7 +1101,7 @@ }, { "cell_type": "markdown", - "id": "1db9f878", + "id": "76d20b98", "metadata": {}, "source": [ "Notice that the three individual outputs `(2,3)`, `2`, and `array([[5, 4],[2, 5], [3,6]])` are themselves output as a tuple. \n", @@ -1114,13 +1114,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "38563574", + "id": "fadb6b45", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.757698Z", - "iopub.status.busy": "2023-08-15T18:48:57.757600Z", - "iopub.status.idle": "2023-08-15T18:48:57.759735Z", - "shell.execute_reply": "2023-08-15T18:48:57.759486Z" + "iopub.execute_input": "2023-08-21T03:36:05.042203Z", + "iopub.status.busy": "2023-08-21T03:36:05.042112Z", + "iopub.status.idle": "2023-08-21T03:36:05.044185Z", + "shell.execute_reply": "2023-08-21T03:36:05.043940Z" } }, "outputs": [ @@ -1142,7 +1142,7 @@ }, { "cell_type": "markdown", - "id": "cdee1904", + "id": "22fab2ce", "metadata": {}, "source": [ "We can also square the elements:" @@ -1151,13 +1151,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "6d7eb044", + "id": "fda3134b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.761258Z", - "iopub.status.busy": "2023-08-15T18:48:57.761155Z", - "iopub.status.idle": "2023-08-15T18:48:57.764201Z", - "shell.execute_reply": "2023-08-15T18:48:57.763942Z" + "iopub.execute_input": "2023-08-21T03:36:05.045709Z", + "iopub.status.busy": "2023-08-21T03:36:05.045601Z", + "iopub.status.idle": "2023-08-21T03:36:05.047665Z", + "shell.execute_reply": "2023-08-21T03:36:05.047412Z" } }, "outputs": [ @@ -1178,7 +1178,7 @@ }, { "cell_type": "markdown", - "id": "7322f04a", + "id": "1278f26b", "metadata": {}, "source": [ "We can compute the square roots using the same notation, raising to the power of $1/2$ instead of 2." @@ -1187,13 +1187,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "dcf49c32", + "id": "52eb335b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.765568Z", - "iopub.status.busy": "2023-08-15T18:48:57.765489Z", - "iopub.status.idle": "2023-08-15T18:48:57.767687Z", - "shell.execute_reply": "2023-08-15T18:48:57.767416Z" + "iopub.execute_input": "2023-08-21T03:36:05.049290Z", + "iopub.status.busy": "2023-08-21T03:36:05.049191Z", + "iopub.status.idle": "2023-08-21T03:36:05.051249Z", + "shell.execute_reply": "2023-08-21T03:36:05.051004Z" }, "lines_to_next_cell": 2 }, @@ -1216,7 +1216,7 @@ }, { "cell_type": "markdown", - "id": "a4ccea4f", + "id": "299a5a85", "metadata": {}, "source": [ "Throughout this book, we will often want to generate random data. \n", @@ -1234,29 +1234,29 @@ { "cell_type": "code", "execution_count": 28, - "id": "dec2f36f", + "id": "ac5e9d29", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.769038Z", - "iopub.status.busy": "2023-08-15T18:48:57.768966Z", - "iopub.status.idle": "2023-08-15T18:48:57.772119Z", - "shell.execute_reply": "2023-08-15T18:48:57.771880Z" + "iopub.execute_input": "2023-08-21T03:36:05.052622Z", + "iopub.status.busy": "2023-08-21T03:36:05.052529Z", + "iopub.status.idle": "2023-08-21T03:36:05.054818Z", + "shell.execute_reply": "2023-08-21T03:36:05.054569Z" } }, "outputs": [ { "data": { "text/plain": [ - "array([ 0.29014042, -1.86056555, -1.03145717, -0.07365371, -0.28189692,\n", - " -0.71611404, 0.05880048, 0.46770064, 0.79645167, -0.0162479 ,\n", - " 0.47130365, 1.05609834, -0.68946797, 1.41358519, 0.73655573,\n", - " -0.54189331, -0.44411721, -1.5708482 , -1.52047945, 1.32694248,\n", - " -1.18915043, -0.32868506, 1.38578859, -0.36928554, 0.42589616,\n", - " -0.4244271 , 0.80408062, 0.43856581, -0.17138868, -0.84893759,\n", - " 1.13046227, 0.22826387, 0.13787643, 1.01904386, -0.29545438,\n", - " -0.62502879, 1.2120335 , -0.82664507, -1.41961403, 1.7012509 ,\n", - " 0.04141695, 2.63161733, 0.40039857, 0.82556693, 0.13357677,\n", - " 0.12961134, 0.62829697, 0.05220314, 0.66114213, 0.68263063])" + "array([-0.97992307, 0.29561095, 0.60481929, -0.20482365, -1.01517527,\n", + " 2.76594674, -0.65748447, 1.27396181, -1.01573869, 1.37373688,\n", + " 0.93524901, -2.41971622, -0.58080017, 0.13777341, 1.35936356,\n", + " 0.61747629, 0.82550921, 0.7943598 , 1.0905082 , 1.80820045,\n", + " -1.31320321, 2.0651966 , -0.78632522, -2.01068042, -1.36562571,\n", + " 1.43466837, -0.16848093, -1.57211786, 0.49888498, -1.86050966,\n", + " 1.08743609, 0.53059599, 0.02172848, -0.41808062, 1.88343414,\n", + " 0.09112977, 1.23489568, 0.53207714, -1.62670383, 1.29859234,\n", + " 0.30311344, 1.01143392, -1.64596169, 1.58876421, -1.85189683,\n", + " -0.53929878, -2.19895144, -0.36691225, 1.03621761, 0.25278481])" ] }, "execution_count": 28, @@ -1271,7 +1271,7 @@ }, { "cell_type": "markdown", - "id": "9c60001d", + "id": "d77cf45a", "metadata": {}, "source": [ "We create an array `y` by adding an independent $N(50,1)$ random variable to each element of `x`." @@ -1280,13 +1280,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "f535fba4", + "id": "55fa905e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.773475Z", - "iopub.status.busy": "2023-08-15T18:48:57.773381Z", - "iopub.status.idle": "2023-08-15T18:48:57.775112Z", - "shell.execute_reply": "2023-08-15T18:48:57.774870Z" + "iopub.execute_input": "2023-08-21T03:36:05.056219Z", + "iopub.status.busy": "2023-08-21T03:36:05.056124Z", + "iopub.status.idle": "2023-08-21T03:36:05.057790Z", + "shell.execute_reply": "2023-08-21T03:36:05.057543Z" }, "lines_to_next_cell": 0 }, @@ -1297,7 +1297,7 @@ }, { "cell_type": "markdown", - "id": "b267be24", + "id": "eacfecc9", "metadata": {}, "source": [ "The `np.corrcoef()` function computes the correlation matrix between `x` and `y`. The off-diagonal elements give the \n", @@ -1307,21 +1307,21 @@ { "cell_type": "code", "execution_count": 30, - "id": "faf3be25", + "id": "fde0dc19", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.776482Z", - "iopub.status.busy": "2023-08-15T18:48:57.776392Z", - "iopub.status.idle": "2023-08-15T18:48:57.778813Z", - "shell.execute_reply": "2023-08-15T18:48:57.778571Z" + "iopub.execute_input": "2023-08-21T03:36:05.059163Z", + "iopub.status.busy": "2023-08-21T03:36:05.059091Z", + "iopub.status.idle": "2023-08-21T03:36:05.061351Z", + "shell.execute_reply": "2023-08-21T03:36:05.061105Z" } }, "outputs": [ { "data": { "text/plain": [ - "array([[1. , 0.76729831],\n", - " [0.76729831, 1. ]])" + "array([[1. , 0.66045794],\n", + " [0.66045794, 1. ]])" ] }, "execution_count": 30, @@ -1335,7 +1335,7 @@ }, { "cell_type": "markdown", - "id": "c6d0e0ce", + "id": "8a594218", "metadata": {}, "source": [ "If you're following along in your own `Jupyter` notebook, then you probably noticed that you got a different set of results when you ran the past few \n", @@ -1347,13 +1347,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "b6a756e0", + "id": "5099cf54", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.780187Z", - "iopub.status.busy": "2023-08-15T18:48:57.780099Z", - "iopub.status.idle": "2023-08-15T18:48:57.782068Z", - "shell.execute_reply": "2023-08-15T18:48:57.781835Z" + "iopub.execute_input": "2023-08-21T03:36:05.062721Z", + "iopub.status.busy": "2023-08-21T03:36:05.062630Z", + "iopub.status.idle": "2023-08-21T03:36:05.064539Z", + "shell.execute_reply": "2023-08-21T03:36:05.064300Z" }, "lines_to_next_cell": 0 }, @@ -1362,8 +1362,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.14627413 4.27059779]\n", - "[ 0.8049877 -5.40386206]\n" + "[-9.63514647 -0.12742473]\n", + "[0.85490033 0.05488893]\n" ] } ], @@ -1374,7 +1374,7 @@ }, { "cell_type": "markdown", - "id": "a1071413", + "id": "2e209118", "metadata": {}, "source": [ " " @@ -1382,7 +1382,7 @@ }, { "cell_type": "markdown", - "id": "31d9911e", + "id": "ed7697a4", "metadata": {}, "source": [ "In order to ensure that our code provides exactly the same results\n", @@ -1398,13 +1398,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "921b634a", + "id": "9d8074e5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.783469Z", - "iopub.status.busy": "2023-08-15T18:48:57.783382Z", - "iopub.status.idle": "2023-08-15T18:48:57.786255Z", - "shell.execute_reply": "2023-08-15T18:48:57.786022Z" + "iopub.execute_input": "2023-08-21T03:36:05.065999Z", + "iopub.status.busy": "2023-08-21T03:36:05.065912Z", + "iopub.status.idle": "2023-08-21T03:36:05.068039Z", + "shell.execute_reply": "2023-08-21T03:36:05.067820Z" } }, "outputs": [ @@ -1426,7 +1426,7 @@ }, { "cell_type": "markdown", - "id": "7a4da703", + "id": "93f826ef", "metadata": {}, "source": [ "Throughout the labs in this book, we use `np.random.default_rng()` whenever we\n", @@ -1444,13 +1444,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "fe28973e", + "id": "e98472df", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.787657Z", - "iopub.status.busy": "2023-08-15T18:48:57.787557Z", - "iopub.status.idle": "2023-08-15T18:48:57.790025Z", - "shell.execute_reply": "2023-08-15T18:48:57.789788Z" + "iopub.execute_input": "2023-08-21T03:36:05.069438Z", + "iopub.status.busy": "2023-08-21T03:36:05.069347Z", + "iopub.status.idle": "2023-08-21T03:36:05.071564Z", + "shell.execute_reply": "2023-08-21T03:36:05.071324Z" }, "lines_to_next_cell": 0 }, @@ -1474,7 +1474,7 @@ }, { "cell_type": "markdown", - "id": "b6092d9c", + "id": "2870d61f", "metadata": {}, "source": [ " \n" @@ -1483,13 +1483,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "04c26742", + "id": "8c2784fd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.791368Z", - "iopub.status.busy": "2023-08-15T18:48:57.791283Z", - "iopub.status.idle": "2023-08-15T18:48:57.793457Z", - "shell.execute_reply": "2023-08-15T18:48:57.793225Z" + "iopub.execute_input": "2023-08-21T03:36:05.072967Z", + "iopub.status.busy": "2023-08-21T03:36:05.072873Z", + "iopub.status.idle": "2023-08-21T03:36:05.075053Z", + "shell.execute_reply": "2023-08-21T03:36:05.074833Z" }, "lines_to_next_cell": 2 }, @@ -1511,7 +1511,7 @@ }, { "cell_type": "markdown", - "id": "b9212f82", + "id": "86261a69", "metadata": {}, "source": [ "Notice that by default `np.var()` divides by the sample size $n$ rather\n", @@ -1521,13 +1521,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "e35cdcbf", + "id": "7e7205f2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.794778Z", - "iopub.status.busy": "2023-08-15T18:48:57.794692Z", - "iopub.status.idle": "2023-08-15T18:48:57.796774Z", - "shell.execute_reply": "2023-08-15T18:48:57.796540Z" + "iopub.execute_input": "2023-08-21T03:36:05.076473Z", + "iopub.status.busy": "2023-08-21T03:36:05.076373Z", + "iopub.status.idle": "2023-08-21T03:36:05.078474Z", + "shell.execute_reply": "2023-08-21T03:36:05.078224Z" } }, "outputs": [ @@ -1548,7 +1548,7 @@ }, { "cell_type": "markdown", - "id": "41eb4c38", + "id": "d4faf901", "metadata": {}, "source": [ "The `np.mean()`, `np.var()`, and `np.std()` functions can also be applied to the rows and columns of a matrix. \n", @@ -1558,13 +1558,13 @@ { "cell_type": "code", "execution_count": 36, - "id": "fbd49766", + "id": "fce06849", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.798111Z", - "iopub.status.busy": "2023-08-15T18:48:57.798040Z", - "iopub.status.idle": "2023-08-15T18:48:57.800173Z", - "shell.execute_reply": "2023-08-15T18:48:57.799940Z" + "iopub.execute_input": "2023-08-21T03:36:05.079856Z", + "iopub.status.busy": "2023-08-21T03:36:05.079755Z", + "iopub.status.idle": "2023-08-21T03:36:05.081961Z", + "shell.execute_reply": "2023-08-21T03:36:05.081709Z" } }, "outputs": [ @@ -1595,7 +1595,7 @@ }, { "cell_type": "markdown", - "id": "5520aeae", + "id": "6cc355d2", "metadata": {}, "source": [ "Since arrays are row-major ordered, the first axis, i.e. `axis=0`, refers to its rows. We pass this argument into the `mean()` method for the object `X`. " @@ -1604,13 +1604,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "f5865859", + "id": "1403ff7a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.801565Z", - "iopub.status.busy": "2023-08-15T18:48:57.801490Z", - "iopub.status.idle": "2023-08-15T18:48:57.803669Z", - "shell.execute_reply": "2023-08-15T18:48:57.803436Z" + "iopub.execute_input": "2023-08-21T03:36:05.083361Z", + "iopub.status.busy": "2023-08-21T03:36:05.083273Z", + "iopub.status.idle": "2023-08-21T03:36:05.085366Z", + "shell.execute_reply": "2023-08-21T03:36:05.085112Z" } }, "outputs": [ @@ -1631,7 +1631,7 @@ }, { "cell_type": "markdown", - "id": "8aedc41d", + "id": "6785c0ec", "metadata": {}, "source": [ "The following yields the same result." @@ -1640,13 +1640,13 @@ { "cell_type": "code", "execution_count": 38, - "id": "e27b30bf", + "id": "7e9255ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.804972Z", - "iopub.status.busy": "2023-08-15T18:48:57.804902Z", - "iopub.status.idle": "2023-08-15T18:48:57.806941Z", - "shell.execute_reply": "2023-08-15T18:48:57.806694Z" + "iopub.execute_input": "2023-08-21T03:36:05.086730Z", + "iopub.status.busy": "2023-08-21T03:36:05.086636Z", + "iopub.status.idle": "2023-08-21T03:36:05.088698Z", + "shell.execute_reply": "2023-08-21T03:36:05.088470Z" }, "lines_to_next_cell": 0 }, @@ -1668,7 +1668,7 @@ }, { "cell_type": "markdown", - "id": "4ee7f516", + "id": "5de246dc", "metadata": {}, "source": [ " " @@ -1676,7 +1676,7 @@ }, { "cell_type": "markdown", - "id": "80ab3209", + "id": "30b002fa", "metadata": {}, "source": [ "## Graphics\n", @@ -1710,13 +1710,13 @@ { "cell_type": "code", "execution_count": 39, - "id": "fed2376c", + "id": "8236e5f7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:57.808301Z", - "iopub.status.busy": "2023-08-15T18:48:57.808228Z", - "iopub.status.idle": "2023-08-15T18:48:58.138590Z", - "shell.execute_reply": "2023-08-15T18:48:58.138241Z" + "iopub.execute_input": "2023-08-21T03:36:05.090105Z", + "iopub.status.busy": "2023-08-21T03:36:05.090015Z", + "iopub.status.idle": "2023-08-21T03:36:05.390499Z", + "shell.execute_reply": "2023-08-21T03:36:05.390143Z" } }, "outputs": [ @@ -1741,7 +1741,7 @@ }, { "cell_type": "markdown", - "id": "9b697b2a", + "id": "bbef67e6", "metadata": {}, "source": [ "We pause here to note that we have *unpacked* the tuple of length two returned by `subplots()` into the two distinct\n", @@ -1752,13 +1752,13 @@ { "cell_type": "code", "execution_count": 40, - "id": "228ee04d", + "id": "ddc9ed4f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.140540Z", - "iopub.status.busy": "2023-08-15T18:48:58.140403Z", - "iopub.status.idle": "2023-08-15T18:48:58.212007Z", - "shell.execute_reply": "2023-08-15T18:48:58.211693Z" + "iopub.execute_input": "2023-08-21T03:36:05.392242Z", + "iopub.status.busy": "2023-08-21T03:36:05.392127Z", + "iopub.status.idle": "2023-08-21T03:36:05.464507Z", + "shell.execute_reply": "2023-08-21T03:36:05.464208Z" } }, "outputs": [ @@ -1781,7 +1781,7 @@ }, { "cell_type": "markdown", - "id": "e040ba84", + "id": "104d6b8f", "metadata": {}, "source": [ "We see that our earlier cell produced a line plot, which is the default. To create a scatterplot, we provide an additional argument to `ax.plot()`, indicating that circles should be displayed." @@ -1790,13 +1790,13 @@ { "cell_type": "code", "execution_count": 41, - "id": "e1dc79d2", + "id": "c64ed600", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.213756Z", - "iopub.status.busy": "2023-08-15T18:48:58.213640Z", - "iopub.status.idle": "2023-08-15T18:48:58.289684Z", - "shell.execute_reply": "2023-08-15T18:48:58.289374Z" + "iopub.execute_input": "2023-08-21T03:36:05.466274Z", + "iopub.status.busy": "2023-08-21T03:36:05.466157Z", + "iopub.status.idle": "2023-08-21T03:36:05.540801Z", + "shell.execute_reply": "2023-08-21T03:36:05.540543Z" }, "lines_to_next_cell": 0 }, @@ -1819,7 +1819,7 @@ }, { "cell_type": "markdown", - "id": "960d0ceb", + "id": "840be2a9", "metadata": {}, "source": [ "Different values\n", @@ -1829,7 +1829,7 @@ }, { "cell_type": "markdown", - "id": "833d6751", + "id": "971b98bd", "metadata": {}, "source": [ "As an alternative, we could use the `ax.scatter()` function to create a scatterplot." @@ -1838,13 +1838,13 @@ { "cell_type": "code", "execution_count": 42, - "id": "d6e6bf34", + "id": "bc6245e2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.291440Z", - "iopub.status.busy": "2023-08-15T18:48:58.291326Z", - "iopub.status.idle": "2023-08-15T18:48:58.373922Z", - "shell.execute_reply": "2023-08-15T18:48:58.373639Z" + "iopub.execute_input": "2023-08-21T03:36:05.542497Z", + "iopub.status.busy": "2023-08-21T03:36:05.542378Z", + "iopub.status.idle": "2023-08-21T03:36:05.621061Z", + "shell.execute_reply": "2023-08-21T03:36:05.620765Z" } }, "outputs": [ @@ -1866,7 +1866,7 @@ }, { "cell_type": "markdown", - "id": "ff56b51f", + "id": "97f36df0", "metadata": {}, "source": [ "Notice that in the code blocks above, we have ended\n", @@ -1878,13 +1878,13 @@ { "cell_type": "code", "execution_count": 43, - "id": "9c1ea81c", + "id": "2454807b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.375715Z", - "iopub.status.busy": "2023-08-15T18:48:58.375585Z", - "iopub.status.idle": "2023-08-15T18:48:58.454702Z", - "shell.execute_reply": "2023-08-15T18:48:58.454405Z" + "iopub.execute_input": "2023-08-21T03:36:05.622738Z", + "iopub.status.busy": "2023-08-21T03:36:05.622624Z", + "iopub.status.idle": "2023-08-21T03:36:05.700853Z", + "shell.execute_reply": "2023-08-21T03:36:05.700564Z" }, "lines_to_next_cell": 0 }, @@ -1892,7 +1892,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -1917,7 +1917,7 @@ }, { "cell_type": "markdown", - "id": "abdc9ceb", + "id": "1230c0a6", "metadata": {}, "source": [ "In what follows, we will use\n", @@ -1930,7 +1930,7 @@ }, { "cell_type": "markdown", - "id": "4b9491f5", + "id": "0ccb9964", "metadata": {}, "source": [ "To label our plot, we make use of the `set_xlabel()`, `set_ylabel()`, and `set_title()` methods\n", @@ -1941,13 +1941,13 @@ { "cell_type": "code", "execution_count": 44, - "id": "e6217bc8", + "id": "1e18a793", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.456512Z", - "iopub.status.busy": "2023-08-15T18:48:58.456386Z", - "iopub.status.idle": "2023-08-15T18:48:58.551777Z", - "shell.execute_reply": "2023-08-15T18:48:58.551466Z" + "iopub.execute_input": "2023-08-21T03:36:05.702667Z", + "iopub.status.busy": "2023-08-21T03:36:05.702545Z", + "iopub.status.idle": "2023-08-21T03:36:05.797850Z", + "shell.execute_reply": "2023-08-21T03:36:05.797545Z" } }, "outputs": [ @@ -1972,7 +1972,7 @@ }, { "cell_type": "markdown", - "id": "e8228a39", + "id": "f2d818ee", "metadata": {}, "source": [ " Having access to the figure object `fig` itself means that we can go in and change some aspects and then redisplay it. Here, we change\n", @@ -1982,13 +1982,13 @@ { "cell_type": "code", "execution_count": 45, - "id": "2feef77c", + "id": "aec3f009", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.553489Z", - "iopub.status.busy": "2023-08-15T18:48:58.553368Z", - "iopub.status.idle": "2023-08-15T18:48:58.616077Z", - "shell.execute_reply": "2023-08-15T18:48:58.615653Z" + "iopub.execute_input": "2023-08-21T03:36:05.799658Z", + "iopub.status.busy": "2023-08-21T03:36:05.799529Z", + "iopub.status.idle": "2023-08-21T03:36:05.861021Z", + "shell.execute_reply": "2023-08-21T03:36:05.860749Z" }, "lines_to_next_cell": 0 }, @@ -2012,7 +2012,7 @@ }, { "cell_type": "markdown", - "id": "d22f3886", + "id": "dee531cc", "metadata": {}, "source": [ " " @@ -2020,7 +2020,7 @@ }, { "cell_type": "markdown", - "id": "e9187ea8", + "id": "011bf802", "metadata": {}, "source": [ "Occasionally we will want to create several plots within a figure. This can be\n", @@ -2036,13 +2036,13 @@ { "cell_type": "code", "execution_count": 46, - "id": "24c9bd3c", + "id": "2cbc7fd4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.617956Z", - "iopub.status.busy": "2023-08-15T18:48:58.617675Z", - "iopub.status.idle": "2023-08-15T18:48:58.904869Z", - "shell.execute_reply": "2023-08-15T18:48:58.904534Z" + "iopub.execute_input": "2023-08-21T03:36:05.862740Z", + "iopub.status.busy": "2023-08-21T03:36:05.862621Z", + "iopub.status.idle": "2023-08-21T03:36:06.143924Z", + "shell.execute_reply": "2023-08-21T03:36:06.143646Z" }, "lines_to_next_cell": 0 }, @@ -2066,7 +2066,7 @@ }, { "cell_type": "markdown", - "id": "7c6bdba5", + "id": "b8ff2e6d", "metadata": {}, "source": [ "We now produce a scatter plot with `'o'` in the second column of the first row and\n", @@ -2076,13 +2076,13 @@ { "cell_type": "code", "execution_count": 47, - "id": "dbe7d87c", + "id": "702f80d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:58.906555Z", - "iopub.status.busy": "2023-08-15T18:48:58.906430Z", - "iopub.status.idle": "2023-08-15T18:48:59.089062Z", - "shell.execute_reply": "2023-08-15T18:48:59.088760Z" + "iopub.execute_input": "2023-08-21T03:36:06.145683Z", + "iopub.status.busy": "2023-08-21T03:36:06.145574Z", + "iopub.status.idle": "2023-08-21T03:36:06.326301Z", + "shell.execute_reply": "2023-08-21T03:36:06.326007Z" }, "lines_to_next_cell": 0 }, @@ -2107,7 +2107,7 @@ }, { "cell_type": "markdown", - "id": "81d8fa9f", + "id": "5b265f8b", "metadata": {}, "source": [ "Type `subplots?` to learn more about \n", @@ -2118,7 +2118,7 @@ }, { "cell_type": "markdown", - "id": "d3760a25", + "id": "1bd7e707", "metadata": {}, "source": [ "To save the output of `fig`, we call its `savefig()`\n", @@ -2129,13 +2129,13 @@ { "cell_type": "code", "execution_count": 48, - "id": "c86bffe6", + "id": "5493d229", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:48:59.090755Z", - "iopub.status.busy": "2023-08-15T18:48:59.090646Z", - "iopub.status.idle": "2023-08-15T18:49:00.169920Z", - "shell.execute_reply": "2023-08-15T18:49:00.169634Z" + "iopub.execute_input": "2023-08-21T03:36:06.327958Z", + "iopub.status.busy": "2023-08-21T03:36:06.327847Z", + "iopub.status.idle": "2023-08-21T03:36:07.432805Z", + "shell.execute_reply": "2023-08-21T03:36:07.432429Z" }, "lines_to_next_cell": 2 }, @@ -2147,7 +2147,7 @@ }, { "cell_type": "markdown", - "id": "fd9dbb47", + "id": "7152d0c7", "metadata": {}, "source": [ "We can continue to modify `fig` using step-by-step updates; for example, we can modify the range of the $x$-axis, re-save the figure, and even re-display it. " @@ -2156,13 +2156,13 @@ { "cell_type": "code", "execution_count": 49, - "id": "07f180e1", + "id": "bd07af12", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.172125Z", - "iopub.status.busy": "2023-08-15T18:49:00.171883Z", - "iopub.status.idle": "2023-08-15T18:49:00.406313Z", - "shell.execute_reply": "2023-08-15T18:49:00.405904Z" + "iopub.execute_input": "2023-08-21T03:36:07.434785Z", + "iopub.status.busy": "2023-08-21T03:36:07.434614Z", + "iopub.status.idle": "2023-08-21T03:36:07.663475Z", + "shell.execute_reply": "2023-08-21T03:36:07.663165Z" } }, "outputs": [ @@ -2186,7 +2186,7 @@ }, { "cell_type": "markdown", - "id": "8e013a6f", + "id": "b5278857", "metadata": {}, "source": [ "We now create some more sophisticated plots. The \n", @@ -2206,13 +2206,13 @@ { "cell_type": "code", "execution_count": 50, - "id": "a465c3aa", + "id": "01019508", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.408153Z", - "iopub.status.busy": "2023-08-15T18:49:00.408030Z", - "iopub.status.idle": "2023-08-15T18:49:00.529270Z", - "shell.execute_reply": "2023-08-15T18:49:00.528966Z" + "iopub.execute_input": "2023-08-21T03:36:07.665255Z", + "iopub.status.busy": "2023-08-21T03:36:07.665128Z", + "iopub.status.idle": "2023-08-21T03:36:07.766569Z", + "shell.execute_reply": "2023-08-21T03:36:07.766244Z" }, "lines_to_next_cell": 0 }, @@ -2238,7 +2238,7 @@ }, { "cell_type": "markdown", - "id": "df35daed", + "id": "9ef3c475", "metadata": {}, "source": [ "We can increase the resolution by adding more levels to the image." @@ -2247,13 +2247,13 @@ { "cell_type": "code", "execution_count": 51, - "id": "469979b0", + "id": "7d08992f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.531056Z", - "iopub.status.busy": "2023-08-15T18:49:00.530927Z", - "iopub.status.idle": "2023-08-15T18:49:00.658628Z", - "shell.execute_reply": "2023-08-15T18:49:00.658295Z" + "iopub.execute_input": "2023-08-21T03:36:07.768244Z", + "iopub.status.busy": "2023-08-21T03:36:07.768130Z", + "iopub.status.idle": "2023-08-21T03:36:07.893027Z", + "shell.execute_reply": "2023-08-21T03:36:07.892660Z" }, "lines_to_next_cell": 0 }, @@ -2276,7 +2276,7 @@ }, { "cell_type": "markdown", - "id": "e6aa9a72", + "id": "8e1d37a2", "metadata": {}, "source": [ "To fine-tune the output of the\n", @@ -2293,13 +2293,13 @@ { "cell_type": "code", "execution_count": 52, - "id": "31092abb", + "id": "1f89d704", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.660299Z", - "iopub.status.busy": "2023-08-15T18:49:00.660207Z", - "iopub.status.idle": "2023-08-15T18:49:00.765408Z", - "shell.execute_reply": "2023-08-15T18:49:00.765121Z" + "iopub.execute_input": "2023-08-21T03:36:07.894802Z", + "iopub.status.busy": "2023-08-21T03:36:07.894695Z", + "iopub.status.idle": "2023-08-21T03:36:07.995202Z", + "shell.execute_reply": "2023-08-21T03:36:07.994863Z" }, "lines_to_next_cell": 2 }, @@ -2322,7 +2322,7 @@ }, { "cell_type": "markdown", - "id": "aba44db2", + "id": "2500a6ec", "metadata": {}, "source": [ "## Sequences and Slice Notation" @@ -2330,7 +2330,7 @@ }, { "cell_type": "markdown", - "id": "f0edc818", + "id": "07001b88", "metadata": {}, "source": [ "As seen above, the\n", @@ -2341,13 +2341,13 @@ { "cell_type": "code", "execution_count": 53, - "id": "4e834c17", + "id": "cd971131", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.767103Z", - "iopub.status.busy": "2023-08-15T18:49:00.766990Z", - "iopub.status.idle": "2023-08-15T18:49:00.769572Z", - "shell.execute_reply": "2023-08-15T18:49:00.769310Z" + "iopub.execute_input": "2023-08-21T03:36:07.996961Z", + "iopub.status.busy": "2023-08-21T03:36:07.996836Z", + "iopub.status.idle": "2023-08-21T03:36:07.999447Z", + "shell.execute_reply": "2023-08-21T03:36:07.999154Z" }, "lines_to_next_cell": 2 }, @@ -2370,7 +2370,7 @@ }, { "cell_type": "markdown", - "id": "e95ff002", + "id": "926f96fc", "metadata": {}, "source": [ "The function `np.arange()`\n", @@ -2381,13 +2381,13 @@ { "cell_type": "code", "execution_count": 54, - "id": "65d67d22", + "id": "aa630d16", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.771217Z", - "iopub.status.busy": "2023-08-15T18:49:00.771065Z", - "iopub.status.idle": "2023-08-15T18:49:00.773408Z", - "shell.execute_reply": "2023-08-15T18:49:00.773113Z" + "iopub.execute_input": "2023-08-21T03:36:08.001028Z", + "iopub.status.busy": "2023-08-21T03:36:08.000925Z", + "iopub.status.idle": "2023-08-21T03:36:08.003185Z", + "shell.execute_reply": "2023-08-21T03:36:08.002908Z" } }, "outputs": [ @@ -2409,7 +2409,7 @@ }, { "cell_type": "markdown", - "id": "09d51db7", + "id": "6908bad7", "metadata": {}, "source": [ "Why isn't $10$ output above? This has to do with *slice* notation in `Python`. \n", @@ -2422,13 +2422,13 @@ { "cell_type": "code", "execution_count": 55, - "id": "676ab564", + "id": "89955ee2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.774895Z", - "iopub.status.busy": "2023-08-15T18:49:00.774790Z", - "iopub.status.idle": "2023-08-15T18:49:00.776727Z", - "shell.execute_reply": "2023-08-15T18:49:00.776499Z" + "iopub.execute_input": "2023-08-21T03:36:08.004703Z", + "iopub.status.busy": "2023-08-21T03:36:08.004594Z", + "iopub.status.idle": "2023-08-21T03:36:08.006692Z", + "shell.execute_reply": "2023-08-21T03:36:08.006429Z" }, "lines_to_next_cell": 0 }, @@ -2450,7 +2450,7 @@ }, { "cell_type": "markdown", - "id": "d51b85fc", + "id": "17d73e4d", "metadata": {}, "source": [ "In the code block above, the notation `3:6` is shorthand for `slice(3,6)` when used inside\n", @@ -2460,13 +2460,13 @@ { "cell_type": "code", "execution_count": 56, - "id": "569f45ab", + "id": "517f592d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.778176Z", - "iopub.status.busy": "2023-08-15T18:49:00.778073Z", - "iopub.status.idle": "2023-08-15T18:49:00.780149Z", - "shell.execute_reply": "2023-08-15T18:49:00.779889Z" + "iopub.execute_input": "2023-08-21T03:36:08.008158Z", + "iopub.status.busy": "2023-08-21T03:36:08.008062Z", + "iopub.status.idle": "2023-08-21T03:36:08.010083Z", + "shell.execute_reply": "2023-08-21T03:36:08.009819Z" } }, "outputs": [ @@ -2487,7 +2487,7 @@ }, { "cell_type": "markdown", - "id": "b82b891c", + "id": "680fe656", "metadata": {}, "source": [ "You might have expected `slice(3,6)` to output the fourth through seventh characters in the text string (recalling that `Python` begins its indexing at zero), but instead it output the fourth through sixth. \n", @@ -2517,7 +2517,7 @@ }, { "cell_type": "markdown", - "id": "46aa45f7", + "id": "522a2761", "metadata": {}, "source": [ "## Indexing Data\n", @@ -2527,13 +2527,13 @@ { "cell_type": "code", "execution_count": 57, - "id": "7cf343bd", + "id": "35927abd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.781654Z", - "iopub.status.busy": "2023-08-15T18:49:00.781547Z", - "iopub.status.idle": "2023-08-15T18:49:00.783829Z", - "shell.execute_reply": "2023-08-15T18:49:00.783563Z" + "iopub.execute_input": "2023-08-21T03:36:08.011518Z", + "iopub.status.busy": "2023-08-21T03:36:08.011425Z", + "iopub.status.idle": "2023-08-21T03:36:08.013558Z", + "shell.execute_reply": "2023-08-21T03:36:08.013317Z" } }, "outputs": [ @@ -2558,7 +2558,7 @@ }, { "cell_type": "markdown", - "id": "a396d5a6", + "id": "27c88984", "metadata": {}, "source": [ "Typing `A[1,2]` retrieves the element corresponding to the second row and third\n", @@ -2568,13 +2568,13 @@ { "cell_type": "code", "execution_count": 58, - "id": "2f4c1b37", + "id": "78ee7f5b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.785421Z", - "iopub.status.busy": "2023-08-15T18:49:00.785315Z", - "iopub.status.idle": "2023-08-15T18:49:00.787314Z", - "shell.execute_reply": "2023-08-15T18:49:00.787066Z" + "iopub.execute_input": "2023-08-21T03:36:08.014962Z", + "iopub.status.busy": "2023-08-21T03:36:08.014871Z", + "iopub.status.idle": "2023-08-21T03:36:08.016938Z", + "shell.execute_reply": "2023-08-21T03:36:08.016689Z" } }, "outputs": [ @@ -2595,7 +2595,7 @@ }, { "cell_type": "markdown", - "id": "839feee0", + "id": "dd65ec1c", "metadata": {}, "source": [ "The first number after the open-bracket symbol `[`\n", @@ -2609,13 +2609,13 @@ { "cell_type": "code", "execution_count": 59, - "id": "a8ef1be5", + "id": "16212696", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.788866Z", - "iopub.status.busy": "2023-08-15T18:49:00.788741Z", - "iopub.status.idle": "2023-08-15T18:49:00.791048Z", - "shell.execute_reply": "2023-08-15T18:49:00.790773Z" + "iopub.execute_input": "2023-08-21T03:36:08.018506Z", + "iopub.status.busy": "2023-08-21T03:36:08.018407Z", + "iopub.status.idle": "2023-08-21T03:36:08.020572Z", + "shell.execute_reply": "2023-08-21T03:36:08.020301Z" } }, "outputs": [ @@ -2637,7 +2637,7 @@ }, { "cell_type": "markdown", - "id": "abe68415", + "id": "0b8b3ce3", "metadata": {}, "source": [ "To select the first and third columns, we pass in `[0,2]` as the second argument in the square brackets.\n", @@ -2648,13 +2648,13 @@ { "cell_type": "code", "execution_count": 60, - "id": "830897a9", + "id": "d5f473d2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.792551Z", - "iopub.status.busy": "2023-08-15T18:49:00.792448Z", - "iopub.status.idle": "2023-08-15T18:49:00.794695Z", - "shell.execute_reply": "2023-08-15T18:49:00.794378Z" + "iopub.execute_input": "2023-08-21T03:36:08.022048Z", + "iopub.status.busy": "2023-08-21T03:36:08.021947Z", + "iopub.status.idle": "2023-08-21T03:36:08.024301Z", + "shell.execute_reply": "2023-08-21T03:36:08.024006Z" } }, "outputs": [ @@ -2678,7 +2678,7 @@ }, { "cell_type": "markdown", - "id": "004f3600", + "id": "471ed1b4", "metadata": {}, "source": [ "Now, suppose that we want to select the submatrix made up of the second and fourth \n", @@ -2689,13 +2689,13 @@ { "cell_type": "code", "execution_count": 61, - "id": "12e88036", + "id": "c89646d6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.796240Z", - "iopub.status.busy": "2023-08-15T18:49:00.796133Z", - "iopub.status.idle": "2023-08-15T18:49:00.798381Z", - "shell.execute_reply": "2023-08-15T18:49:00.798108Z" + "iopub.execute_input": "2023-08-21T03:36:08.025864Z", + "iopub.status.busy": "2023-08-21T03:36:08.025745Z", + "iopub.status.idle": "2023-08-21T03:36:08.027990Z", + "shell.execute_reply": "2023-08-21T03:36:08.027692Z" } }, "outputs": [ @@ -2716,7 +2716,7 @@ }, { "cell_type": "markdown", - "id": "9b978348", + "id": "9cbf1ff9", "metadata": {}, "source": [ " Oops --- what happened? We got a one-dimensional array of length two identical to" @@ -2725,13 +2725,13 @@ { "cell_type": "code", "execution_count": 62, - "id": "c4098144", + "id": "87f6b4f2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.799924Z", - "iopub.status.busy": "2023-08-15T18:49:00.799822Z", - "iopub.status.idle": "2023-08-15T18:49:00.801953Z", - "shell.execute_reply": "2023-08-15T18:49:00.801652Z" + "iopub.execute_input": "2023-08-21T03:36:08.029541Z", + "iopub.status.busy": "2023-08-21T03:36:08.029439Z", + "iopub.status.idle": "2023-08-21T03:36:08.031650Z", + "shell.execute_reply": "2023-08-21T03:36:08.031378Z" } }, "outputs": [ @@ -2752,7 +2752,7 @@ }, { "cell_type": "markdown", - "id": "2dcd98eb", + "id": "9a93dc96", "metadata": {}, "source": [ " Similarly, the following code fails to extract the submatrix comprised of the second and fourth rows and the first, third, and fourth columns:" @@ -2761,13 +2761,13 @@ { "cell_type": "code", "execution_count": 63, - "id": "b4d6bd49", + "id": "5da5bda8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.803507Z", - "iopub.status.busy": "2023-08-15T18:49:00.803385Z", - "iopub.status.idle": "2023-08-15T18:49:00.827278Z", - "shell.execute_reply": "2023-08-15T18:49:00.826930Z" + "iopub.execute_input": "2023-08-21T03:36:08.033127Z", + "iopub.status.busy": "2023-08-21T03:36:08.033016Z", + "iopub.status.idle": "2023-08-21T03:36:08.056275Z", + "shell.execute_reply": "2023-08-21T03:36:08.056014Z" } }, "outputs": [ @@ -2789,7 +2789,7 @@ }, { "cell_type": "markdown", - "id": "dcac422f", + "id": "f4fd2f83", "metadata": {}, "source": [ "We can see what has gone wrong here. When supplied with two indexing lists, the `numpy` interpretation is that these provide pairs of $i,j$ indices for a series of entries. That is why the pair of lists must have the same length. However, that was not our intent, since we are looking for a submatrix.\n", @@ -2800,13 +2800,13 @@ { "cell_type": "code", "execution_count": 64, - "id": "e46c3267", + "id": "ac48a95b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.828896Z", - "iopub.status.busy": "2023-08-15T18:49:00.828781Z", - "iopub.status.idle": "2023-08-15T18:49:00.831271Z", - "shell.execute_reply": "2023-08-15T18:49:00.831008Z" + "iopub.execute_input": "2023-08-21T03:36:08.057926Z", + "iopub.status.busy": "2023-08-21T03:36:08.057815Z", + "iopub.status.idle": "2023-08-21T03:36:08.060231Z", + "shell.execute_reply": "2023-08-21T03:36:08.059980Z" }, "lines_to_next_cell": 0 }, @@ -2829,7 +2829,7 @@ }, { "cell_type": "markdown", - "id": "d14bb320", + "id": "5e8388aa", "metadata": {}, "source": [ " " @@ -2837,7 +2837,7 @@ }, { "cell_type": "markdown", - "id": "a3d7e667", + "id": "a09467cd", "metadata": {}, "source": [ "There are more efficient ways of achieving the same result.\n", @@ -2849,13 +2849,13 @@ { "cell_type": "code", "execution_count": 65, - "id": "96d2355e", + "id": "ee195cc4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.832787Z", - "iopub.status.busy": "2023-08-15T18:49:00.832688Z", - "iopub.status.idle": "2023-08-15T18:49:00.835031Z", - "shell.execute_reply": "2023-08-15T18:49:00.834787Z" + "iopub.execute_input": "2023-08-21T03:36:08.061798Z", + "iopub.status.busy": "2023-08-21T03:36:08.061686Z", + "iopub.status.idle": "2023-08-21T03:36:08.063979Z", + "shell.execute_reply": "2023-08-21T03:36:08.063715Z" }, "lines_to_next_cell": 2 }, @@ -2879,7 +2879,7 @@ }, { "cell_type": "markdown", - "id": "d462675f", + "id": "b7177cb9", "metadata": {}, "source": [ "Alternatively, we can subset matrices efficiently using slices.\n", @@ -2892,13 +2892,13 @@ { "cell_type": "code", "execution_count": 66, - "id": "326ef740", + "id": "48917bb5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.836551Z", - "iopub.status.busy": "2023-08-15T18:49:00.836441Z", - "iopub.status.idle": "2023-08-15T18:49:00.838682Z", - "shell.execute_reply": "2023-08-15T18:49:00.838419Z" + "iopub.execute_input": "2023-08-21T03:36:08.067067Z", + "iopub.status.busy": "2023-08-21T03:36:08.066955Z", + "iopub.status.idle": "2023-08-21T03:36:08.069279Z", + "shell.execute_reply": "2023-08-21T03:36:08.068966Z" }, "lines_to_next_cell": 0 }, @@ -2921,7 +2921,7 @@ }, { "cell_type": "markdown", - "id": "e2eeed28", + "id": "697c5ab0", "metadata": {}, "source": [ " " @@ -2929,7 +2929,7 @@ }, { "cell_type": "markdown", - "id": "5139b99f", + "id": "c647dbf0", "metadata": {}, "source": [ "Why are we able to retrieve a submatrix directly using slices but not using lists?\n", @@ -2951,7 +2951,7 @@ }, { "cell_type": "markdown", - "id": "417c640e", + "id": "2dce8961", "metadata": {}, "source": [ "### Boolean Indexing\n", @@ -2962,13 +2962,13 @@ { "cell_type": "code", "execution_count": 67, - "id": "62ad65f2", + "id": "5d4caf22", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.840331Z", - "iopub.status.busy": "2023-08-15T18:49:00.840207Z", - "iopub.status.idle": "2023-08-15T18:49:00.842474Z", - "shell.execute_reply": "2023-08-15T18:49:00.842219Z" + "iopub.execute_input": "2023-08-21T03:36:08.070903Z", + "iopub.status.busy": "2023-08-21T03:36:08.070799Z", + "iopub.status.idle": "2023-08-21T03:36:08.072959Z", + "shell.execute_reply": "2023-08-21T03:36:08.072701Z" }, "lines_to_next_cell": 0 }, @@ -2991,7 +2991,7 @@ }, { "cell_type": "markdown", - "id": "117d1718", + "id": "d83fadb5", "metadata": {}, "source": [ "We now set two of the elements to `True`. " @@ -3000,13 +3000,13 @@ { "cell_type": "code", "execution_count": 68, - "id": "683f49a3", + "id": "348820e3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.843979Z", - "iopub.status.busy": "2023-08-15T18:49:00.843879Z", - "iopub.status.idle": "2023-08-15T18:49:00.846073Z", - "shell.execute_reply": "2023-08-15T18:49:00.845769Z" + "iopub.execute_input": "2023-08-21T03:36:08.074422Z", + "iopub.status.busy": "2023-08-21T03:36:08.074322Z", + "iopub.status.idle": "2023-08-21T03:36:08.076398Z", + "shell.execute_reply": "2023-08-21T03:36:08.076106Z" } }, "outputs": [ @@ -3028,7 +3028,7 @@ }, { "cell_type": "markdown", - "id": "5036e0f2", + "id": "a0fb487d", "metadata": {}, "source": [ "Note that the elements of `keep_rows`, when viewed as integers, are the same as the\n", @@ -3039,13 +3039,13 @@ { "cell_type": "code", "execution_count": 69, - "id": "ede73656", + "id": "4aafe45b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.847573Z", - "iopub.status.busy": "2023-08-15T18:49:00.847472Z", - "iopub.status.idle": "2023-08-15T18:49:00.849818Z", - "shell.execute_reply": "2023-08-15T18:49:00.849531Z" + "iopub.execute_input": "2023-08-21T03:36:08.077952Z", + "iopub.status.busy": "2023-08-21T03:36:08.077853Z", + "iopub.status.idle": "2023-08-21T03:36:08.080163Z", + "shell.execute_reply": "2023-08-21T03:36:08.079901Z" } }, "outputs": [ @@ -3066,7 +3066,7 @@ }, { "cell_type": "markdown", - "id": "45587df2", + "id": "603c0c53", "metadata": {}, "source": [ "(Here, the function `np.all()` has checked whether\n", @@ -3075,7 +3075,7 @@ }, { "cell_type": "markdown", - "id": "cd3e2b53", + "id": "b0a449d1", "metadata": {}, "source": [ " However, even though `np.array([0,1,0,1])` and `keep_rows` are equal according to `==`, they index different sets of rows!\n", @@ -3085,13 +3085,13 @@ { "cell_type": "code", "execution_count": 70, - "id": "f65c75d6", + "id": "1be6a588", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.851339Z", - "iopub.status.busy": "2023-08-15T18:49:00.851241Z", - "iopub.status.idle": "2023-08-15T18:49:00.853566Z", - "shell.execute_reply": "2023-08-15T18:49:00.853301Z" + "iopub.execute_input": "2023-08-21T03:36:08.081637Z", + "iopub.status.busy": "2023-08-21T03:36:08.081537Z", + "iopub.status.idle": "2023-08-21T03:36:08.083796Z", + "shell.execute_reply": "2023-08-21T03:36:08.083505Z" } }, "outputs": [ @@ -3115,7 +3115,7 @@ }, { "cell_type": "markdown", - "id": "12479298", + "id": "e45bbebe", "metadata": {}, "source": [ " By contrast, `keep_rows` retrieves only the second and fourth rows of `A` --- i.e. the rows for which the Boolean equals `TRUE`. " @@ -3124,13 +3124,13 @@ { "cell_type": "code", "execution_count": 71, - "id": "31a8ff76", + "id": "e83da57b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.855023Z", - "iopub.status.busy": "2023-08-15T18:49:00.854922Z", - "iopub.status.idle": "2023-08-15T18:49:00.857083Z", - "shell.execute_reply": "2023-08-15T18:49:00.856833Z" + "iopub.execute_input": "2023-08-21T03:36:08.085319Z", + "iopub.status.busy": "2023-08-21T03:36:08.085216Z", + "iopub.status.idle": "2023-08-21T03:36:08.087359Z", + "shell.execute_reply": "2023-08-21T03:36:08.087095Z" } }, "outputs": [ @@ -3152,7 +3152,7 @@ }, { "cell_type": "markdown", - "id": "40932333", + "id": "374d34a7", "metadata": {}, "source": [ "This example shows that Booleans and integers are treated differently by `numpy`." @@ -3160,7 +3160,7 @@ }, { "cell_type": "markdown", - "id": "8751cf99", + "id": "25db74bf", "metadata": {}, "source": [ "We again make use of the `np.ix_()` function\n", @@ -3171,13 +3171,13 @@ { "cell_type": "code", "execution_count": 72, - "id": "c91f330e", + "id": "09675294", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.858714Z", - "iopub.status.busy": "2023-08-15T18:49:00.858613Z", - "iopub.status.idle": "2023-08-15T18:49:00.861203Z", - "shell.execute_reply": "2023-08-15T18:49:00.860913Z" + "iopub.execute_input": "2023-08-21T03:36:08.088904Z", + "iopub.status.busy": "2023-08-21T03:36:08.088804Z", + "iopub.status.idle": "2023-08-21T03:36:08.091266Z", + "shell.execute_reply": "2023-08-21T03:36:08.090985Z" } }, "outputs": [ @@ -3202,7 +3202,7 @@ }, { "cell_type": "markdown", - "id": "e8b9619b", + "id": "0166c179", "metadata": {}, "source": [ "We can also mix a list with an array of Booleans in the arguments to `np.ix_()`:" @@ -3211,13 +3211,13 @@ { "cell_type": "code", "execution_count": 73, - "id": "fb53de70", + "id": "a85614e4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.862684Z", - "iopub.status.busy": "2023-08-15T18:49:00.862579Z", - "iopub.status.idle": "2023-08-15T18:49:00.864973Z", - "shell.execute_reply": "2023-08-15T18:49:00.864715Z" + "iopub.execute_input": "2023-08-21T03:36:08.092757Z", + "iopub.status.busy": "2023-08-21T03:36:08.092664Z", + "iopub.status.idle": "2023-08-21T03:36:08.094873Z", + "shell.execute_reply": "2023-08-21T03:36:08.094581Z" }, "lines_to_next_cell": 0 }, @@ -3241,7 +3241,7 @@ }, { "cell_type": "markdown", - "id": "18f16a0a", + "id": "f6a338f1", "metadata": {}, "source": [ " " @@ -3249,7 +3249,7 @@ }, { "cell_type": "markdown", - "id": "f6e932f4", + "id": "b3541e0c", "metadata": {}, "source": [ "For more details on indexing in `numpy`, readers are referred\n", @@ -3258,7 +3258,7 @@ }, { "cell_type": "markdown", - "id": "8c59352f", + "id": "ab75f168", "metadata": {}, "source": [ "## Loading Data\n", @@ -3275,7 +3275,7 @@ }, { "cell_type": "markdown", - "id": "4417d4d4", + "id": "ca018d13", "metadata": {}, "source": [ "### Reading in a Data Set\n", @@ -3294,7 +3294,7 @@ }, { "cell_type": "markdown", - "id": "f9fd0896", + "id": "b76342df", "metadata": {}, "source": [ "We will begin by reading in `Auto.csv`, available on the book website. This is a comma-separated file, and can be read in using `pd.read_csv()`: " @@ -3303,13 +3303,13 @@ { "cell_type": "code", "execution_count": 74, - "id": "cc87ed8c", + "id": "ff81e644", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:00.866686Z", - "iopub.status.busy": "2023-08-15T18:49:00.866579Z", - "iopub.status.idle": "2023-08-15T18:49:01.152579Z", - "shell.execute_reply": "2023-08-15T18:49:01.152290Z" + "iopub.execute_input": "2023-08-21T03:36:08.096546Z", + "iopub.status.busy": "2023-08-21T03:36:08.096449Z", + "iopub.status.idle": "2023-08-21T03:36:08.317557Z", + "shell.execute_reply": "2023-08-21T03:36:08.317278Z" } }, "outputs": [ @@ -3526,7 +3526,7 @@ }, { "cell_type": "markdown", - "id": "5c4cf682", + "id": "42d6a799", "metadata": {}, "source": [ "The book website also has a whitespace-delimited version of this data, called `Auto.data`. This can be read in as follows:" @@ -3535,13 +3535,13 @@ { "cell_type": "code", "execution_count": 75, - "id": "69b34e00", + "id": "5b45aa7f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.154192Z", - "iopub.status.busy": "2023-08-15T18:49:01.154082Z", - "iopub.status.idle": "2023-08-15T18:49:01.157257Z", - "shell.execute_reply": "2023-08-15T18:49:01.157026Z" + "iopub.execute_input": "2023-08-21T03:36:08.319194Z", + "iopub.status.busy": "2023-08-21T03:36:08.319082Z", + "iopub.status.idle": "2023-08-21T03:36:08.322312Z", + "shell.execute_reply": "2023-08-21T03:36:08.322063Z" }, "lines_to_next_cell": 0 }, @@ -3552,7 +3552,7 @@ }, { "cell_type": "markdown", - "id": "61b12987", + "id": "f942c457", "metadata": {}, "source": [ " Both `Auto.csv` and `Auto.data` are simply text\n", @@ -3563,7 +3563,7 @@ }, { "cell_type": "markdown", - "id": "8de34877", + "id": "1aceff38", "metadata": {}, "source": [ "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: " @@ -3572,13 +3572,13 @@ { "cell_type": "code", "execution_count": 76, - "id": "c45b3e32", + "id": "413f626a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.158784Z", - "iopub.status.busy": "2023-08-15T18:49:01.158680Z", - "iopub.status.idle": "2023-08-15T18:49:01.161285Z", - "shell.execute_reply": "2023-08-15T18:49:01.161037Z" + "iopub.execute_input": "2023-08-21T03:36:08.323776Z", + "iopub.status.busy": "2023-08-21T03:36:08.323686Z", + "iopub.status.idle": "2023-08-21T03:36:08.326230Z", + "shell.execute_reply": "2023-08-21T03:36:08.325989Z" }, "lines_to_next_cell": 0 }, @@ -3611,7 +3611,7 @@ }, { "cell_type": "markdown", - "id": "7b5df8f9", + "id": "fd11e757", "metadata": {}, "source": [ "We see that the `dtype` of this column is `object`. \n", @@ -3623,13 +3623,13 @@ { "cell_type": "code", "execution_count": 77, - "id": "fdac0ada", + "id": "57b86346", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.162652Z", - "iopub.status.busy": "2023-08-15T18:49:01.162556Z", - "iopub.status.idle": "2023-08-15T18:49:01.164858Z", - "shell.execute_reply": "2023-08-15T18:49:01.164626Z" + "iopub.execute_input": "2023-08-21T03:36:08.327630Z", + "iopub.status.busy": "2023-08-21T03:36:08.327527Z", + "iopub.status.idle": "2023-08-21T03:36:08.329871Z", + "shell.execute_reply": "2023-08-21T03:36:08.329620Z" }, "lines_to_next_cell": 0 }, @@ -3664,7 +3664,7 @@ }, { "cell_type": "markdown", - "id": "dc6e92aa", + "id": "f0aee233", "metadata": {}, "source": [ "We see the culprit is the value `?`, which is being used to encode missing values.\n", @@ -3673,7 +3673,7 @@ }, { "cell_type": "markdown", - "id": "f15457ad", + "id": "b7b032d4", "metadata": {}, "source": [ "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n", @@ -3684,13 +3684,13 @@ { "cell_type": "code", "execution_count": 78, - "id": "883e0b73", + "id": "a9698b26", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.166263Z", - "iopub.status.busy": "2023-08-15T18:49:01.166173Z", - "iopub.status.idle": "2023-08-15T18:49:01.169588Z", - "shell.execute_reply": "2023-08-15T18:49:01.169351Z" + "iopub.execute_input": "2023-08-21T03:36:08.331328Z", + "iopub.status.busy": "2023-08-21T03:36:08.331230Z", + "iopub.status.idle": "2023-08-21T03:36:08.334947Z", + "shell.execute_reply": "2023-08-21T03:36:08.334715Z" }, "lines_to_next_cell": 2 }, @@ -3715,7 +3715,7 @@ }, { "cell_type": "markdown", - "id": "cbc53f2a", + "id": "13cb364e", "metadata": {}, "source": [ "The `Auto.shape` attribute tells us that the data has 397\n", @@ -3725,13 +3725,13 @@ { "cell_type": "code", "execution_count": 79, - "id": "c733c1e8", + "id": "4877cb2c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.170985Z", - "iopub.status.busy": "2023-08-15T18:49:01.170894Z", - "iopub.status.idle": "2023-08-15T18:49:01.173006Z", - "shell.execute_reply": "2023-08-15T18:49:01.172777Z" + "iopub.execute_input": "2023-08-21T03:36:08.336400Z", + "iopub.status.busy": "2023-08-21T03:36:08.336322Z", + "iopub.status.idle": "2023-08-21T03:36:08.338340Z", + "shell.execute_reply": "2023-08-21T03:36:08.338101Z" } }, "outputs": [ @@ -3752,7 +3752,7 @@ }, { "cell_type": "markdown", - "id": "94c32cfb", + "id": "3fdc6f47", "metadata": {}, "source": [ "There are\n", @@ -3764,13 +3764,13 @@ { "cell_type": "code", "execution_count": 80, - "id": "0e0e9956", + "id": "2ba1d33d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.174378Z", - "iopub.status.busy": "2023-08-15T18:49:01.174286Z", - "iopub.status.idle": "2023-08-15T18:49:01.177052Z", - "shell.execute_reply": "2023-08-15T18:49:01.176811Z" + "iopub.execute_input": "2023-08-21T03:36:08.339714Z", + "iopub.status.busy": "2023-08-21T03:36:08.339640Z", + "iopub.status.idle": "2023-08-21T03:36:08.342116Z", + "shell.execute_reply": "2023-08-21T03:36:08.341883Z" }, "lines_to_next_cell": 2 }, @@ -3793,7 +3793,7 @@ }, { "cell_type": "markdown", - "id": "7250498b", + "id": "ac9748d9", "metadata": {}, "source": [ "### Basics of Selecting Rows and Columns\n", @@ -3804,13 +3804,13 @@ { "cell_type": "code", "execution_count": 81, - "id": "0477b4ff", + "id": "3d03baab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.178507Z", - "iopub.status.busy": "2023-08-15T18:49:01.178399Z", - "iopub.status.idle": "2023-08-15T18:49:01.180581Z", - "shell.execute_reply": "2023-08-15T18:49:01.180288Z" + "iopub.execute_input": "2023-08-21T03:36:08.343548Z", + "iopub.status.busy": "2023-08-21T03:36:08.343471Z", + "iopub.status.idle": "2023-08-21T03:36:08.345627Z", + "shell.execute_reply": "2023-08-21T03:36:08.345379Z" }, "lines_to_next_cell": 2 }, @@ -3835,7 +3835,7 @@ }, { "cell_type": "markdown", - "id": "c2084300", + "id": "d24d4d42", "metadata": {}, "source": [ "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n", @@ -3848,13 +3848,13 @@ { "cell_type": "code", "execution_count": 82, - "id": "a989e541", + "id": "410b4dd7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.182149Z", - "iopub.status.busy": "2023-08-15T18:49:01.182047Z", - "iopub.status.idle": "2023-08-15T18:49:01.186385Z", - "shell.execute_reply": "2023-08-15T18:49:01.186138Z" + "iopub.execute_input": "2023-08-21T03:36:08.347031Z", + "iopub.status.busy": "2023-08-21T03:36:08.346955Z", + "iopub.status.idle": "2023-08-21T03:36:08.351303Z", + "shell.execute_reply": "2023-08-21T03:36:08.351081Z" }, "lines_to_next_cell": 0 }, @@ -3955,7 +3955,7 @@ }, { "cell_type": "markdown", - "id": "b5af114e", + "id": "4ea0be7b", "metadata": {}, "source": [ "Similarly, an array of Booleans can be used to subset the rows:" @@ -3964,13 +3964,13 @@ { "cell_type": "code", "execution_count": 83, - "id": "d336e7ce", + "id": "3540804d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.187796Z", - "iopub.status.busy": "2023-08-15T18:49:01.187719Z", - "iopub.status.idle": "2023-08-15T18:49:01.200825Z", - "shell.execute_reply": "2023-08-15T18:49:01.200533Z" + "iopub.execute_input": "2023-08-21T03:36:08.352671Z", + "iopub.status.busy": "2023-08-21T03:36:08.352588Z", + "iopub.status.idle": "2023-08-21T03:36:08.365290Z", + "shell.execute_reply": "2023-08-21T03:36:08.365017Z" }, "lines_to_next_cell": 0 }, @@ -4842,7 +4842,7 @@ }, { "cell_type": "markdown", - "id": "2f09c4df", + "id": "a02221a2", "metadata": {}, "source": [ "However, if we pass in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*. " @@ -4851,13 +4851,13 @@ { "cell_type": "code", "execution_count": 84, - "id": "88e9a63b", + "id": "66d174f1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.202619Z", - "iopub.status.busy": "2023-08-15T18:49:01.202479Z", - "iopub.status.idle": "2023-08-15T18:49:01.207774Z", - "shell.execute_reply": "2023-08-15T18:49:01.207524Z" + "iopub.execute_input": "2023-08-21T03:36:08.366746Z", + "iopub.status.busy": "2023-08-21T03:36:08.366648Z", + "iopub.status.idle": "2023-08-21T03:36:08.371142Z", + "shell.execute_reply": "2023-08-21T03:36:08.370889Z" }, "lines_to_next_cell": 0 }, @@ -4976,7 +4976,7 @@ }, { "cell_type": "markdown", - "id": "b372687d", + "id": "54bef6a3", "metadata": {}, "source": [ "Since we did not specify an *index* column when we loaded our data frame, the rows are labeled using integers\n", @@ -4986,13 +4986,13 @@ { "cell_type": "code", "execution_count": 85, - "id": "aed049d8", + "id": "52789c77", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.209353Z", - "iopub.status.busy": "2023-08-15T18:49:01.209247Z", - "iopub.status.idle": "2023-08-15T18:49:01.211692Z", - "shell.execute_reply": "2023-08-15T18:49:01.211381Z" + "iopub.execute_input": "2023-08-21T03:36:08.372548Z", + "iopub.status.busy": "2023-08-21T03:36:08.372468Z", + "iopub.status.idle": "2023-08-21T03:36:08.374676Z", + "shell.execute_reply": "2023-08-21T03:36:08.374427Z" }, "lines_to_next_cell": 0 }, @@ -5017,7 +5017,7 @@ }, { "cell_type": "markdown", - "id": "f18f3695", + "id": "3f5fcb26", "metadata": {}, "source": [ "We can use the\n", @@ -5027,13 +5027,13 @@ { "cell_type": "code", "execution_count": 86, - "id": "c93cefd3", + "id": "d83650bf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.213226Z", - "iopub.status.busy": "2023-08-15T18:49:01.213124Z", - "iopub.status.idle": "2023-08-15T18:49:01.219346Z", - "shell.execute_reply": "2023-08-15T18:49:01.219084Z" + "iopub.execute_input": "2023-08-21T03:36:08.376096Z", + "iopub.status.busy": "2023-08-21T03:36:08.376016Z", + "iopub.status.idle": "2023-08-21T03:36:08.382023Z", + "shell.execute_reply": "2023-08-21T03:36:08.381780Z" } }, "outputs": [ @@ -5251,13 +5251,13 @@ { "cell_type": "code", "execution_count": 87, - "id": "84947a78", + "id": "880d79d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.220731Z", - "iopub.status.busy": "2023-08-15T18:49:01.220656Z", - "iopub.status.idle": "2023-08-15T18:49:01.222770Z", - "shell.execute_reply": "2023-08-15T18:49:01.222521Z" + "iopub.execute_input": "2023-08-21T03:36:08.383342Z", + "iopub.status.busy": "2023-08-21T03:36:08.383266Z", + "iopub.status.idle": "2023-08-21T03:36:08.385348Z", + "shell.execute_reply": "2023-08-21T03:36:08.385111Z" }, "lines_to_next_cell": 0 }, @@ -5281,7 +5281,7 @@ }, { "cell_type": "markdown", - "id": "fc160806", + "id": "dbee53b8", "metadata": {}, "source": [ "We see that the column `'name'` is no longer there.\n", @@ -5294,13 +5294,13 @@ { "cell_type": "code", "execution_count": 88, - "id": "7ee59fae", + "id": "c01f4095", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.224183Z", - "iopub.status.busy": "2023-08-15T18:49:01.224083Z", - "iopub.status.idle": "2023-08-15T18:49:01.229143Z", - "shell.execute_reply": "2023-08-15T18:49:01.228880Z" + "iopub.execute_input": "2023-08-21T03:36:08.386800Z", + "iopub.status.busy": "2023-08-21T03:36:08.386719Z", + "iopub.status.idle": "2023-08-21T03:36:08.390900Z", + "shell.execute_reply": "2023-08-21T03:36:08.390675Z" }, "lines_to_next_cell": 0 }, @@ -5398,7 +5398,7 @@ }, { "cell_type": "markdown", - "id": "232fe34c", + "id": "29688cab", "metadata": {}, "source": [ "As an alternative to using the index name, we could retrieve the 4th and 5th rows of `Auto` using the `{iloc[]`} method:" @@ -5407,13 +5407,13 @@ { "cell_type": "code", "execution_count": 89, - "id": "4b5a0e01", + "id": "a4202eb8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.230515Z", - "iopub.status.busy": "2023-08-15T18:49:01.230412Z", - "iopub.status.idle": "2023-08-15T18:49:01.234647Z", - "shell.execute_reply": "2023-08-15T18:49:01.234396Z" + "iopub.execute_input": "2023-08-21T03:36:08.392405Z", + "iopub.status.busy": "2023-08-21T03:36:08.392322Z", + "iopub.status.idle": "2023-08-21T03:36:08.396509Z", + "shell.execute_reply": "2023-08-21T03:36:08.396244Z" }, "lines_to_next_cell": 0 }, @@ -5510,7 +5510,7 @@ }, { "cell_type": "markdown", - "id": "6c409a01", + "id": "5427ede0", "metadata": {}, "source": [ "We can also use it to retrieve the 1st, 3rd and and 4th columns of `Auto_re`:" @@ -5519,13 +5519,13 @@ { "cell_type": "code", "execution_count": 90, - "id": "8814803e", + "id": "948b2d07", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.236161Z", - "iopub.status.busy": "2023-08-15T18:49:01.236068Z", - "iopub.status.idle": "2023-08-15T18:49:01.240725Z", - "shell.execute_reply": "2023-08-15T18:49:01.240476Z" + "iopub.execute_input": "2023-08-21T03:36:08.397917Z", + "iopub.status.busy": "2023-08-21T03:36:08.397838Z", + "iopub.status.idle": "2023-08-21T03:36:08.402447Z", + "shell.execute_reply": "2023-08-21T03:36:08.402192Z" }, "lines_to_next_cell": 0 }, @@ -5663,7 +5663,7 @@ }, { "cell_type": "markdown", - "id": "f02f0d24", + "id": "b83d56eb", "metadata": {}, "source": [ "We can extract the 4th and 5th rows, as well as the 1st, 3rd and 4th columns, using\n", @@ -5673,13 +5673,13 @@ { "cell_type": "code", "execution_count": 91, - "id": "c7567dd0", + "id": "1cfdcc5c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.242172Z", - "iopub.status.busy": "2023-08-15T18:49:01.242093Z", - "iopub.status.idle": "2023-08-15T18:49:01.245535Z", - "shell.execute_reply": "2023-08-15T18:49:01.245278Z" + "iopub.execute_input": "2023-08-21T03:36:08.403773Z", + "iopub.status.busy": "2023-08-21T03:36:08.403692Z", + "iopub.status.idle": "2023-08-21T03:36:08.407036Z", + "shell.execute_reply": "2023-08-21T03:36:08.406794Z" }, "lines_to_next_cell": 0 }, @@ -5751,7 +5751,7 @@ }, { "cell_type": "markdown", - "id": "3073ab09", + "id": "2bde6514", "metadata": {}, "source": [ "Index entries need not be unique: there are several cars in the data frame named `ford galaxie 500`." @@ -5760,13 +5760,13 @@ { "cell_type": "code", "execution_count": 92, - "id": "63638341", + "id": "fd9c5cda", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.246909Z", - "iopub.status.busy": "2023-08-15T18:49:01.246831Z", - "iopub.status.idle": "2023-08-15T18:49:01.250226Z", - "shell.execute_reply": "2023-08-15T18:49:01.249979Z" + "iopub.execute_input": "2023-08-21T03:36:08.408461Z", + "iopub.status.busy": "2023-08-21T03:36:08.408387Z", + "iopub.status.idle": "2023-08-21T03:36:08.411673Z", + "shell.execute_reply": "2023-08-21T03:36:08.411429Z" }, "lines_to_next_cell": 0 }, @@ -5840,7 +5840,7 @@ }, { "cell_type": "markdown", - "id": "2fbe4a9b", + "id": "4d097282", "metadata": {}, "source": [ "### More on Selecting Rows and Columns\n", @@ -5853,13 +5853,13 @@ { "cell_type": "code", "execution_count": 93, - "id": "57d6de11", + "id": "6d431cb5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.251615Z", - "iopub.status.busy": "2023-08-15T18:49:01.251531Z", - "iopub.status.idle": "2023-08-15T18:49:01.257033Z", - "shell.execute_reply": "2023-08-15T18:49:01.256798Z" + "iopub.execute_input": "2023-08-21T03:36:08.413092Z", + "iopub.status.busy": "2023-08-21T03:36:08.413016Z", + "iopub.status.idle": "2023-08-21T03:36:08.418535Z", + "shell.execute_reply": "2023-08-21T03:36:08.418303Z" }, "lines_to_next_cell": 2 }, @@ -6264,7 +6264,7 @@ }, { "cell_type": "markdown", - "id": "a58067ce", + "id": "838a03e0", "metadata": {}, "source": [ "To do this more concisely, we can use an anonymous function called a `lambda`: " @@ -6273,13 +6273,13 @@ { "cell_type": "code", "execution_count": 94, - "id": "ed95866c", + "id": "fac41ce1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.258351Z", - "iopub.status.busy": "2023-08-15T18:49:01.258278Z", - "iopub.status.idle": "2023-08-15T18:49:01.263791Z", - "shell.execute_reply": "2023-08-15T18:49:01.263537Z" + "iopub.execute_input": "2023-08-21T03:36:08.419882Z", + "iopub.status.busy": "2023-08-21T03:36:08.419806Z", + "iopub.status.idle": "2023-08-21T03:36:08.425319Z", + "shell.execute_reply": "2023-08-21T03:36:08.425073Z" }, "lines_to_next_cell": 0 }, @@ -6683,7 +6683,7 @@ }, { "cell_type": "markdown", - "id": "13bb6d1f", + "id": "08e61254", "metadata": {}, "source": [ "The `lambda` call creates a function that takes a single\n", @@ -6697,13 +6697,13 @@ { "cell_type": "code", "execution_count": 95, - "id": "e3725c98", + "id": "b0885654", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.265199Z", - "iopub.status.busy": "2023-08-15T18:49:01.265115Z", - "iopub.status.idle": "2023-08-15T18:49:01.269816Z", - "shell.execute_reply": "2023-08-15T18:49:01.269583Z" + "iopub.execute_input": "2023-08-21T03:36:08.426701Z", + "iopub.status.busy": "2023-08-21T03:36:08.426621Z", + "iopub.status.idle": "2023-08-21T03:36:08.431378Z", + "shell.execute_reply": "2023-08-21T03:36:08.431148Z" }, "lines_to_next_cell": 0 }, @@ -6965,7 +6965,7 @@ }, { "cell_type": "markdown", - "id": "6da7a099", + "id": "d87fc459", "metadata": {}, "source": [ "The symbol `&` computes an element-wise *and* operation.\n", @@ -6977,13 +6977,13 @@ { "cell_type": "code", "execution_count": 96, - "id": "f617c333", + "id": "213945a6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.271218Z", - "iopub.status.busy": "2023-08-15T18:49:01.271142Z", - "iopub.status.idle": "2023-08-15T18:49:01.277457Z", - "shell.execute_reply": "2023-08-15T18:49:01.277189Z" + "iopub.execute_input": "2023-08-21T03:36:08.432780Z", + "iopub.status.busy": "2023-08-21T03:36:08.432696Z", + "iopub.status.idle": "2023-08-21T03:36:08.438473Z", + "shell.execute_reply": "2023-08-21T03:36:08.438250Z" }, "lines_to_next_cell": 0 }, @@ -7349,7 +7349,7 @@ }, { "cell_type": "markdown", - "id": "924428a0", + "id": "8a940fd1", "metadata": {}, "source": [ "Here, the symbol `|` computes an element-wise *or* operation.\n", @@ -7368,13 +7368,13 @@ { "cell_type": "code", "execution_count": 97, - "id": "17f13a72", + "id": "a3c4060a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.278887Z", - "iopub.status.busy": "2023-08-15T18:49:01.278782Z", - "iopub.status.idle": "2023-08-15T18:49:01.280597Z", - "shell.execute_reply": "2023-08-15T18:49:01.280357Z" + "iopub.execute_input": "2023-08-21T03:36:08.439902Z", + "iopub.status.busy": "2023-08-21T03:36:08.439825Z", + "iopub.status.idle": "2023-08-21T03:36:08.441707Z", + "shell.execute_reply": "2023-08-21T03:36:08.441464Z" }, "lines_to_next_cell": 0 }, @@ -7396,7 +7396,7 @@ }, { "cell_type": "markdown", - "id": "15f0c1b1", + "id": "9117e3a1", "metadata": {}, "source": [ "The indented code beneath the line with the `for` statement is run\n", @@ -7412,13 +7412,13 @@ { "cell_type": "code", "execution_count": 98, - "id": "07e6a011", + "id": "f2bffb69", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.282018Z", - "iopub.status.busy": "2023-08-15T18:49:01.281918Z", - "iopub.status.idle": "2023-08-15T18:49:01.283785Z", - "shell.execute_reply": "2023-08-15T18:49:01.283534Z" + "iopub.execute_input": "2023-08-21T03:36:08.443349Z", + "iopub.status.busy": "2023-08-21T03:36:08.443241Z", + "iopub.status.idle": "2023-08-21T03:36:08.445173Z", + "shell.execute_reply": "2023-08-21T03:36:08.444935Z" }, "lines_to_next_cell": 0 }, @@ -7441,7 +7441,7 @@ }, { "cell_type": "markdown", - "id": "fd8124cb", + "id": "9f99e85b", "metadata": {}, "source": [ "Above, we summed over each combination of `value` and `weight`.\n", @@ -7464,13 +7464,13 @@ { "cell_type": "code", "execution_count": 99, - "id": "377ab16f", + "id": "ee827a53", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.285159Z", - "iopub.status.busy": "2023-08-15T18:49:01.285060Z", - "iopub.status.idle": "2023-08-15T18:49:01.286977Z", - "shell.execute_reply": "2023-08-15T18:49:01.286756Z" + "iopub.execute_input": "2023-08-21T03:36:08.446610Z", + "iopub.status.busy": "2023-08-21T03:36:08.446520Z", + "iopub.status.idle": "2023-08-21T03:36:08.448323Z", + "shell.execute_reply": "2023-08-21T03:36:08.448070Z" } }, "outputs": [ @@ -7492,7 +7492,7 @@ }, { "cell_type": "markdown", - "id": "345a595b", + "id": "dec18466", "metadata": {}, "source": [ "### String Formatting\n", @@ -7519,13 +7519,13 @@ { "cell_type": "code", "execution_count": 100, - "id": "91276344", + "id": "3a097fbc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.288402Z", - "iopub.status.busy": "2023-08-15T18:49:01.288304Z", - "iopub.status.idle": "2023-08-15T18:49:01.293596Z", - "shell.execute_reply": "2023-08-15T18:49:01.293335Z" + "iopub.execute_input": "2023-08-21T03:36:08.449714Z", + "iopub.status.busy": "2023-08-21T03:36:08.449623Z", + "iopub.status.idle": "2023-08-21T03:36:08.454097Z", + "shell.execute_reply": "2023-08-21T03:36:08.453813Z" }, "lines_to_next_cell": 2 }, @@ -7615,13 +7615,13 @@ { "cell_type": "code", "execution_count": 101, - "id": "c655fcd1", + "id": "e064e170", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.294914Z", - "iopub.status.busy": "2023-08-15T18:49:01.294824Z", - "iopub.status.idle": "2023-08-15T18:49:01.297196Z", - "shell.execute_reply": "2023-08-15T18:49:01.296959Z" + "iopub.execute_input": "2023-08-21T03:36:08.455528Z", + "iopub.status.busy": "2023-08-21T03:36:08.455447Z", + "iopub.status.idle": "2023-08-21T03:36:08.457783Z", + "shell.execute_reply": "2023-08-21T03:36:08.457543Z" }, "lines_to_next_cell": 0 }, @@ -7647,7 +7647,7 @@ }, { "cell_type": "markdown", - "id": "7267a350", + "id": "7a3e4dd8", "metadata": {}, "source": [ "We see that the `template.format()` method expects two arguments `{0}`\n", @@ -7661,7 +7661,7 @@ }, { "cell_type": "markdown", - "id": "763fe84a", + "id": "d8fd496a", "metadata": {}, "source": [ "## Additional Graphical and Numerical Summaries\n", @@ -7672,13 +7672,13 @@ { "cell_type": "code", "execution_count": 102, - "id": "cd9e0046", + "id": "c915ca52", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.298614Z", - "iopub.status.busy": "2023-08-15T18:49:01.298525Z", - "iopub.status.idle": "2023-08-15T18:49:01.391402Z", - "shell.execute_reply": "2023-08-15T18:49:01.391128Z" + "iopub.execute_input": "2023-08-21T03:36:08.459248Z", + "iopub.status.busy": "2023-08-21T03:36:08.459155Z", + "iopub.status.idle": "2023-08-21T03:36:08.552220Z", + "shell.execute_reply": "2023-08-21T03:36:08.551914Z" }, "lines_to_next_cell": 0 }, @@ -7712,7 +7712,7 @@ }, { "cell_type": "markdown", - "id": "0f0f16b8", + "id": "63d47021", "metadata": {}, "source": [ "We can address this by accessing the columns directly:" @@ -7721,13 +7721,13 @@ { "cell_type": "code", "execution_count": 103, - "id": "49d239e5", + "id": "65cd6d02", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.393117Z", - "iopub.status.busy": "2023-08-15T18:49:01.393003Z", - "iopub.status.idle": "2023-08-15T18:49:01.483359Z", - "shell.execute_reply": "2023-08-15T18:49:01.483063Z" + "iopub.execute_input": "2023-08-21T03:36:08.553877Z", + "iopub.status.busy": "2023-08-21T03:36:08.553770Z", + "iopub.status.idle": "2023-08-21T03:36:08.640715Z", + "shell.execute_reply": "2023-08-21T03:36:08.640411Z" }, "lines_to_next_cell": 0 }, @@ -7750,7 +7750,7 @@ }, { "cell_type": "markdown", - "id": "154e405c", + "id": "726836f0", "metadata": {}, "source": [ "Alternatively, we can use the `plot()` method with the call `Auto.plot()`.\n", @@ -7763,13 +7763,13 @@ { "cell_type": "code", "execution_count": 104, - "id": "5c892a6a", + "id": "76b5c0b1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.485105Z", - "iopub.status.busy": "2023-08-15T18:49:01.484987Z", - "iopub.status.idle": "2023-08-15T18:49:01.581202Z", - "shell.execute_reply": "2023-08-15T18:49:01.580905Z" + "iopub.execute_input": "2023-08-21T03:36:08.642311Z", + "iopub.status.busy": "2023-08-21T03:36:08.642208Z", + "iopub.status.idle": "2023-08-21T03:36:08.737860Z", + "shell.execute_reply": "2023-08-21T03:36:08.737565Z" }, "lines_to_next_cell": 0 }, @@ -7792,7 +7792,7 @@ }, { "cell_type": "markdown", - "id": "74ed42da", + "id": "69c46251", "metadata": {}, "source": [ "If we want to save\n", @@ -7803,13 +7803,13 @@ { "cell_type": "code", "execution_count": 105, - "id": "92a626b0", + "id": "183a2c2b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.582802Z", - "iopub.status.busy": "2023-08-15T18:49:01.582688Z", - "iopub.status.idle": "2023-08-15T18:49:01.623968Z", - "shell.execute_reply": "2023-08-15T18:49:01.623675Z" + "iopub.execute_input": "2023-08-21T03:36:08.739490Z", + "iopub.status.busy": "2023-08-21T03:36:08.739374Z", + "iopub.status.idle": "2023-08-21T03:36:08.779690Z", + "shell.execute_reply": "2023-08-21T03:36:08.779420Z" } }, "outputs": [], @@ -7820,7 +7820,7 @@ }, { "cell_type": "markdown", - "id": "7c47c9a8", + "id": "6f10cb46", "metadata": {}, "source": [ "We can further instruct the data frame to plot to a particular axes object. In this\n", @@ -7834,13 +7834,13 @@ { "cell_type": "code", "execution_count": 106, - "id": "bf9771b9", + "id": "75fbb981", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.625586Z", - "iopub.status.busy": "2023-08-15T18:49:01.625474Z", - "iopub.status.idle": "2023-08-15T18:49:01.795305Z", - "shell.execute_reply": "2023-08-15T18:49:01.795005Z" + "iopub.execute_input": "2023-08-21T03:36:08.781309Z", + "iopub.status.busy": "2023-08-21T03:36:08.781225Z", + "iopub.status.idle": "2023-08-21T03:36:08.949186Z", + "shell.execute_reply": "2023-08-21T03:36:08.948871Z" } }, "outputs": [ @@ -7862,7 +7862,7 @@ }, { "cell_type": "markdown", - "id": "a27e61c6", + "id": "53ffc0da", "metadata": {}, "source": [ "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`. " @@ -7870,7 +7870,7 @@ }, { "cell_type": "markdown", - "id": "7d4e028a", + "id": "1c4705e0", "metadata": {}, "source": [ "We now consider the `cylinders` variable. Typing in `Auto.cylinders.dtype` reveals that it is being treated as a quantitative variable. \n", @@ -7882,13 +7882,13 @@ { "cell_type": "code", "execution_count": 107, - "id": "745d2c5f", + "id": "55b3a1cc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.797116Z", - "iopub.status.busy": "2023-08-15T18:49:01.796991Z", - "iopub.status.idle": "2023-08-15T18:49:01.802339Z", - "shell.execute_reply": "2023-08-15T18:49:01.802067Z" + "iopub.execute_input": "2023-08-21T03:36:08.950967Z", + "iopub.status.busy": "2023-08-21T03:36:08.950852Z", + "iopub.status.idle": "2023-08-21T03:36:08.953871Z", + "shell.execute_reply": "2023-08-21T03:36:08.953602Z" }, "lines_to_next_cell": 0 }, @@ -7911,7 +7911,7 @@ }, { "cell_type": "markdown", - "id": "d9c6dc9e", + "id": "adc75408", "metadata": {}, "source": [ " Now that `cylinders` is qualitative, we can display it using\n", @@ -7921,13 +7921,13 @@ { "cell_type": "code", "execution_count": 108, - "id": "8b1069bc", + "id": "f3d88794", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.803789Z", - "iopub.status.busy": "2023-08-15T18:49:01.803689Z", - "iopub.status.idle": "2023-08-15T18:49:01.912221Z", - "shell.execute_reply": "2023-08-15T18:49:01.911925Z" + "iopub.execute_input": "2023-08-21T03:36:08.955394Z", + "iopub.status.busy": "2023-08-21T03:36:08.955297Z", + "iopub.status.idle": "2023-08-21T03:36:09.060746Z", + "shell.execute_reply": "2023-08-21T03:36:09.060425Z" } }, "outputs": [ @@ -7949,7 +7949,7 @@ }, { "cell_type": "markdown", - "id": "f96288b4", + "id": "62d6582f", "metadata": {}, "source": [ "The `hist()` method can be used to plot a *histogram*." @@ -7958,13 +7958,13 @@ { "cell_type": "code", "execution_count": 109, - "id": "d9faa85a", + "id": "eea49f5b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:01.914205Z", - "iopub.status.busy": "2023-08-15T18:49:01.914091Z", - "iopub.status.idle": "2023-08-15T18:49:02.015255Z", - "shell.execute_reply": "2023-08-15T18:49:02.014946Z" + "iopub.execute_input": "2023-08-21T03:36:09.062393Z", + "iopub.status.busy": "2023-08-21T03:36:09.062272Z", + "iopub.status.idle": "2023-08-21T03:36:09.164120Z", + "shell.execute_reply": "2023-08-21T03:36:09.163816Z" }, "lines_to_next_cell": 0 }, @@ -7987,7 +7987,7 @@ }, { "cell_type": "markdown", - "id": "8f743bb2", + "id": "c5a5933c", "metadata": {}, "source": [ "The color of the bars and the number of bins can be changed:" @@ -7996,13 +7996,13 @@ { "cell_type": "code", "execution_count": 110, - "id": "59d4a0b6", + "id": "d5bcfff8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:02.016993Z", - "iopub.status.busy": "2023-08-15T18:49:02.016874Z", - "iopub.status.idle": "2023-08-15T18:49:02.114274Z", - "shell.execute_reply": "2023-08-15T18:49:02.113961Z" + "iopub.execute_input": "2023-08-21T03:36:09.165860Z", + "iopub.status.busy": "2023-08-21T03:36:09.165745Z", + "iopub.status.idle": "2023-08-21T03:36:09.262378Z", + "shell.execute_reply": "2023-08-21T03:36:09.262095Z" }, "lines_to_next_cell": 0 }, @@ -8025,7 +8025,7 @@ }, { "cell_type": "markdown", - "id": "59d4f72f", + "id": "60c36b6c", "metadata": {}, "source": [ " See `Auto.hist?` for more plotting\n", @@ -8038,13 +8038,13 @@ { "cell_type": "code", "execution_count": 111, - "id": "fb5ed57d", + "id": "edb66cae", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:02.115927Z", - "iopub.status.busy": "2023-08-15T18:49:02.115815Z", - "iopub.status.idle": "2023-08-15T18:49:03.055465Z", - "shell.execute_reply": "2023-08-15T18:49:03.055199Z" + "iopub.execute_input": "2023-08-21T03:36:09.264050Z", + "iopub.status.busy": "2023-08-21T03:36:09.263936Z", + "iopub.status.idle": "2023-08-21T03:36:10.182139Z", + "shell.execute_reply": "2023-08-21T03:36:10.181842Z" }, "lines_to_next_cell": 0 }, @@ -8066,7 +8066,7 @@ }, { "cell_type": "markdown", - "id": "7f671845", + "id": "0b162bd9", "metadata": {}, "source": [ " We can also produce scatterplots\n", @@ -8076,13 +8076,13 @@ { "cell_type": "code", "execution_count": 112, - "id": "8e117e38", + "id": "4f5d25d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:03.057178Z", - "iopub.status.busy": "2023-08-15T18:49:03.057073Z", - "iopub.status.idle": "2023-08-15T18:49:03.291546Z", - "shell.execute_reply": "2023-08-15T18:49:03.291171Z" + "iopub.execute_input": "2023-08-21T03:36:10.183868Z", + "iopub.status.busy": "2023-08-21T03:36:10.183754Z", + "iopub.status.idle": "2023-08-21T03:36:10.401318Z", + "shell.execute_reply": "2023-08-21T03:36:10.401033Z" }, "lines_to_next_cell": 0 }, @@ -8106,7 +8106,7 @@ }, { "cell_type": "markdown", - "id": "97921078", + "id": "8cae5dfc", "metadata": {}, "source": [ "The `describe()` method produces a numerical summary of each column in a data frame." @@ -8115,13 +8115,13 @@ { "cell_type": "code", "execution_count": 113, - "id": "9e0b12f2", + "id": "ce7b23e2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:03.293251Z", - "iopub.status.busy": "2023-08-15T18:49:03.293122Z", - "iopub.status.idle": "2023-08-15T18:49:03.299440Z", - "shell.execute_reply": "2023-08-15T18:49:03.299052Z" + "iopub.execute_input": "2023-08-21T03:36:10.403067Z", + "iopub.status.busy": "2023-08-21T03:36:10.402953Z", + "iopub.status.idle": "2023-08-21T03:36:10.408957Z", + "shell.execute_reply": "2023-08-21T03:36:10.408683Z" }, "lines_to_next_cell": 0 }, @@ -8219,7 +8219,7 @@ }, { "cell_type": "markdown", - "id": "93fc12de", + "id": "d5042294", "metadata": {}, "source": [ "We can also produce a summary of just a single column." @@ -8228,13 +8228,13 @@ { "cell_type": "code", "execution_count": 114, - "id": "ba4aa0f6", + "id": "a6545d2f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-15T18:49:03.300968Z", - "iopub.status.busy": "2023-08-15T18:49:03.300854Z", - "iopub.status.idle": "2023-08-15T18:49:03.304996Z", - "shell.execute_reply": "2023-08-15T18:49:03.304715Z" + "iopub.execute_input": "2023-08-21T03:36:10.410500Z", + "iopub.status.busy": "2023-08-21T03:36:10.410393Z", + "iopub.status.idle": "2023-08-21T03:36:10.414627Z", + "shell.execute_reply": "2023-08-21T03:36:10.414350Z" }, "lines_to_next_cell": 0 }, @@ -8265,7 +8265,7 @@ }, { "cell_type": "markdown", - "id": "c029ae61", + "id": "c2ea7f81", "metadata": {}, "source": [ "To exit `Jupyter`, select `File / Close and Halt`.\n", diff --git a/Ch03-linreg-lab.Rmd b/Ch03-linreg-lab.Rmd index 4ea50d2..930176c 100644 --- a/Ch03-linreg-lab.Rmd +++ b/Ch03-linreg-lab.Rmd @@ -15,8 +15,6 @@ jupyter: # Chapter 3 - - # Lab: Linear Regression ## Importing packages @@ -277,7 +275,7 @@ value of 25.05 for `medv` when `lstat` equals 10), but the latter are substantially wider. Next we will plot `medv` and `lstat` -using `DataFrame.plot.scatter()`, +using `DataFrame.plot.scatter()`, \definelongblankMR{plot.scatter()}{plot.slashslashscatter()} and wish to add the regression line to the resulting plot. @@ -399,7 +397,7 @@ Notice how we have compacted the first line into a succinct expression describin The `Boston` data set contains 12 variables, and so it would be cumbersome to have to type all of these in order to perform a regression using all of the predictors. -Instead, we can use the following short-hand: +Instead, we can use the following short-hand:\definelongblankMR{columns.drop()}{columns.slashslashdrop()} ```{python} terms = Boston.columns.drop('medv') diff --git a/Ch03-linreg-lab.ipynb b/Ch03-linreg-lab.ipynb index 1b5c61b..f039622 100644 --- a/Ch03-linreg-lab.ipynb +++ b/Ch03-linreg-lab.ipynb @@ -2,17 +2,16 @@ "cells": [ { "cell_type": "markdown", - "id": "82bce88a", + "id": "cfd81b34", "metadata": {}, "source": [ "\n", - "# Chapter 3\n", - "\n" + "# Chapter 3" ] }, { "cell_type": "markdown", - "id": "dcffdceb", + "id": "52e6256f", "metadata": {}, "source": [ "# Lab: Linear Regression\n", @@ -25,13 +24,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "ca5277a6", + "id": "b18c1628", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:29.402170Z", - "iopub.status.busy": "2023-08-07T00:18:29.401851Z", - "iopub.status.idle": "2023-08-07T00:18:29.884854Z", - "shell.execute_reply": "2023-08-07T00:18:29.884559Z" + "iopub.execute_input": "2023-08-21T02:28:52.978537Z", + "iopub.status.busy": "2023-08-21T02:28:52.978403Z", + "iopub.status.idle": "2023-08-21T02:28:53.456488Z", + "shell.execute_reply": "2023-08-21T02:28:53.456172Z" }, "lines_to_next_cell": 2 }, @@ -44,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "95b0ae8c", + "id": "735a61a6", "metadata": {}, "source": [ "### New imports\n", @@ -58,13 +57,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "675f24e6", + "id": "a10a1d7d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:29.886827Z", - "iopub.status.busy": "2023-08-07T00:18:29.886604Z", - "iopub.status.idle": "2023-08-07T00:18:30.513216Z", - "shell.execute_reply": "2023-08-07T00:18:30.512918Z" + "iopub.execute_input": "2023-08-21T02:28:53.458574Z", + "iopub.status.busy": "2023-08-21T02:28:53.458356Z", + "iopub.status.idle": "2023-08-21T02:28:54.074306Z", + "shell.execute_reply": "2023-08-21T02:28:54.073950Z" }, "lines_to_next_cell": 0 }, @@ -75,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "1fb0799f", + "id": "4c10ec5f", "metadata": {}, "source": [ " We will provide relevant details about the\n", @@ -91,13 +90,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "a0ee23c2", + "id": "756c0524", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.515096Z", - "iopub.status.busy": "2023-08-07T00:18:30.514948Z", - "iopub.status.idle": "2023-08-07T00:18:30.517757Z", - "shell.execute_reply": "2023-08-07T00:18:30.517502Z" + "iopub.execute_input": "2023-08-21T02:28:54.076206Z", + "iopub.status.busy": "2023-08-21T02:28:54.076055Z", + "iopub.status.idle": "2023-08-21T02:28:54.079277Z", + "shell.execute_reply": "2023-08-21T02:28:54.079022Z" } }, "outputs": [], @@ -109,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "dbfd346e", + "id": "686eb4ee", "metadata": {}, "source": [ "As one of the import statements above is quite a long line, we inserted a line break `\\` to\n", @@ -122,13 +121,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "b35eb887", + "id": "2c370a6e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.519241Z", - "iopub.status.busy": "2023-08-07T00:18:30.519152Z", - "iopub.status.idle": "2023-08-07T00:18:30.676458Z", - "shell.execute_reply": "2023-08-07T00:18:30.675986Z" + "iopub.execute_input": "2023-08-21T02:28:54.080812Z", + "iopub.status.busy": "2023-08-21T02:28:54.080717Z", + "iopub.status.idle": "2023-08-21T02:28:54.212489Z", + "shell.execute_reply": "2023-08-21T02:28:54.212195Z" } }, "outputs": [], @@ -141,7 +140,7 @@ }, { "cell_type": "markdown", - "id": "84a8177e", + "id": "163af4a1", "metadata": {}, "source": [ "### Inspecting Objects and Namespaces\n", @@ -154,13 +153,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "961908f7", + "id": "43bcb3a9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.678594Z", - "iopub.status.busy": "2023-08-07T00:18:30.678317Z", - "iopub.status.idle": "2023-08-07T00:18:30.682161Z", - "shell.execute_reply": "2023-08-07T00:18:30.681889Z" + "iopub.execute_input": "2023-08-21T02:28:54.214288Z", + "iopub.status.busy": "2023-08-21T02:28:54.214131Z", + "iopub.status.idle": "2023-08-21T02:28:54.218521Z", + "shell.execute_reply": "2023-08-21T02:28:54.217256Z" }, "lines_to_next_cell": 0 }, @@ -218,7 +217,7 @@ }, { "cell_type": "markdown", - "id": "3efc6d99", + "id": "3d936b00", "metadata": {}, "source": [ " This shows you everything that `Python` can find at the top level.\n", @@ -235,13 +234,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "662caa15", + "id": "cb81bfb0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.683690Z", - "iopub.status.busy": "2023-08-07T00:18:30.683590Z", - "iopub.status.idle": "2023-08-07T00:18:30.686573Z", - "shell.execute_reply": "2023-08-07T00:18:30.686323Z" + "iopub.execute_input": "2023-08-21T02:28:54.228152Z", + "iopub.status.busy": "2023-08-21T02:28:54.227968Z", + "iopub.status.idle": "2023-08-21T02:28:54.231888Z", + "shell.execute_reply": "2023-08-21T02:28:54.231418Z" }, "lines_to_next_cell": 0 }, @@ -429,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "49bdd416", + "id": "c5693cc8", "metadata": {}, "source": [ " This indicates that the object `A.sum` exists. In this case it is a method\n", @@ -439,13 +438,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "ebb7d126", + "id": "29e9bdab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.688076Z", - "iopub.status.busy": "2023-08-07T00:18:30.687980Z", - "iopub.status.idle": "2023-08-07T00:18:30.690330Z", - "shell.execute_reply": "2023-08-07T00:18:30.690041Z" + "iopub.execute_input": "2023-08-21T02:28:54.233644Z", + "iopub.status.busy": "2023-08-21T02:28:54.233538Z", + "iopub.status.idle": "2023-08-21T02:28:54.236077Z", + "shell.execute_reply": "2023-08-21T02:28:54.235804Z" }, "lines_to_next_cell": 0 }, @@ -467,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "3b9db985", + "id": "d000df26", "metadata": {}, "source": [ " " @@ -475,7 +474,7 @@ }, { "cell_type": "markdown", - "id": "9fbed3f3", + "id": "2fff2f42", "metadata": {}, "source": [ "## Simple Linear Regression\n", @@ -497,13 +496,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "1ea46cee", + "id": "72a15de5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.693238Z", - "iopub.status.busy": "2023-08-07T00:18:30.693135Z", - "iopub.status.idle": "2023-08-07T00:18:30.700216Z", - "shell.execute_reply": "2023-08-07T00:18:30.699965Z" + "iopub.execute_input": "2023-08-21T02:28:54.237580Z", + "iopub.status.busy": "2023-08-21T02:28:54.237492Z", + "iopub.status.idle": "2023-08-21T02:28:54.242855Z", + "shell.execute_reply": "2023-08-21T02:28:54.242574Z" } }, "outputs": [ @@ -527,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "a8cceee6", + "id": "d4fefb35", "metadata": {}, "source": [ "Type `Boston?` to find out more about these data.\n", @@ -541,13 +540,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "26c0ba88", + "id": "26837cfe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.701638Z", - "iopub.status.busy": "2023-08-07T00:18:30.701559Z", - "iopub.status.idle": "2023-08-07T00:18:30.706894Z", - "shell.execute_reply": "2023-08-07T00:18:30.706635Z" + "iopub.execute_input": "2023-08-21T02:28:54.244219Z", + "iopub.status.busy": "2023-08-21T02:28:54.244127Z", + "iopub.status.idle": "2023-08-21T02:28:54.249714Z", + "shell.execute_reply": "2023-08-21T02:28:54.249432Z" } }, "outputs": [ @@ -622,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "5ac7f183", + "id": "73cb41ca", "metadata": {}, "source": [ "We extract the response, and fit the model." @@ -631,13 +630,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "d4dd511b", + "id": "c70cbdb5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.708348Z", - "iopub.status.busy": "2023-08-07T00:18:30.708254Z", - "iopub.status.idle": "2023-08-07T00:18:30.710731Z", - "shell.execute_reply": "2023-08-07T00:18:30.710460Z" + "iopub.execute_input": "2023-08-21T02:28:54.251263Z", + "iopub.status.busy": "2023-08-21T02:28:54.251136Z", + "iopub.status.idle": "2023-08-21T02:28:54.253671Z", + "shell.execute_reply": "2023-08-21T02:28:54.253397Z" }, "lines_to_next_cell": 0 }, @@ -650,7 +649,7 @@ }, { "cell_type": "markdown", - "id": "ddf44723", + "id": "e87fbd51", "metadata": {}, "source": [ "Note that `sm.OLS()` does\n", @@ -666,13 +665,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "eef9f8e3", + "id": "6f2d7c78", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.712145Z", - "iopub.status.busy": "2023-08-07T00:18:30.712039Z", - "iopub.status.idle": "2023-08-07T00:18:30.771318Z", - "shell.execute_reply": "2023-08-07T00:18:30.771048Z" + "iopub.execute_input": "2023-08-21T02:28:54.255193Z", + "iopub.status.busy": "2023-08-21T02:28:54.255089Z", + "iopub.status.idle": "2023-08-21T02:28:54.314793Z", + "shell.execute_reply": "2023-08-21T02:28:54.314504Z" }, "lines_to_next_cell": 2 }, @@ -740,7 +739,7 @@ }, { "cell_type": "markdown", - "id": "db916e19", + "id": "cf6f045c", "metadata": {}, "source": [ "Before we describe other methods for working with fitted models, we outline a more useful and general framework for constructing a model matrix~`X`.\n", @@ -777,13 +776,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "557170d4", + "id": "cdec4294", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.772908Z", - "iopub.status.busy": "2023-08-07T00:18:30.772767Z", - "iopub.status.idle": "2023-08-07T00:18:30.777533Z", - "shell.execute_reply": "2023-08-07T00:18:30.777292Z" + "iopub.execute_input": "2023-08-21T02:28:54.316521Z", + "iopub.status.busy": "2023-08-21T02:28:54.316363Z", + "iopub.status.idle": "2023-08-21T02:28:54.323224Z", + "shell.execute_reply": "2023-08-21T02:28:54.322970Z" }, "lines_to_next_cell": 0 }, @@ -860,7 +859,7 @@ }, { "cell_type": "markdown", - "id": "86615813", + "id": "1de3a91e", "metadata": {}, "source": [ "In this simple case, the `fit()` method does very little; it simply checks that the variable `'lstat'` specified in `design` exists in `Boston`. Then `transform()` constructs the model matrix with two columns: an `intercept` and the variable `lstat`.\n", @@ -872,13 +871,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "b83ec097", + "id": "edf2efcb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.778980Z", - "iopub.status.busy": "2023-08-07T00:18:30.778901Z", - "iopub.status.idle": "2023-08-07T00:18:30.783086Z", - "shell.execute_reply": "2023-08-07T00:18:30.782821Z" + "iopub.execute_input": "2023-08-21T02:28:54.324750Z", + "iopub.status.busy": "2023-08-21T02:28:54.324644Z", + "iopub.status.idle": "2023-08-21T02:28:54.330945Z", + "shell.execute_reply": "2023-08-21T02:28:54.330680Z" }, "lines_to_next_cell": 0 }, @@ -954,7 +953,7 @@ }, { "cell_type": "markdown", - "id": "c6becbcb", + "id": "4eab8574", "metadata": {}, "source": [ "Note that, as in the previous code chunk when the two steps were done separately, the `design` object is changed as a result of the `fit()` operation. The power of this pipeline will become clearer when we fit more complex models that involve interactions and transformations." @@ -962,7 +961,7 @@ }, { "cell_type": "markdown", - "id": "e097120f", + "id": "7336a90b", "metadata": {}, "source": [ "Let's return to our fitted regression model.\n", @@ -976,13 +975,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "d4dce5f6", + "id": "49fc8992", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.784600Z", - "iopub.status.busy": "2023-08-07T00:18:30.784504Z", - "iopub.status.idle": "2023-08-07T00:18:30.793701Z", - "shell.execute_reply": "2023-08-07T00:18:30.793458Z" + "iopub.execute_input": "2023-08-21T02:28:54.332402Z", + "iopub.status.busy": "2023-08-21T02:28:54.332319Z", + "iopub.status.idle": "2023-08-21T02:28:54.341724Z", + "shell.execute_reply": "2023-08-21T02:28:54.341409Z" } }, "outputs": [ @@ -1001,10 +1000,10 @@ " Method: Least Squares F-statistic: 601.6\n", "\n", "\n", - " Date: Sun, 06 Aug 2023 Prob (F-statistic): 5.08e-88\n", + " Date: Sun, 20 Aug 2023 Prob (F-statistic): 5.08e-88\n", "\n", "\n", - " Time: 17:18:30 Log-Likelihood: -1641.5\n", + " Time: 19:28:54 Log-Likelihood: -1641.5\n", "\n", "\n", " No. Observations: 506 AIC: 3287.\n", @@ -1052,8 +1051,8 @@ "\\textbf{Dep. Variable:} & medv & \\textbf{ R-squared: } & 0.544 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.543 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 601.6 \\\\\n", - "\\textbf{Date:} & Sun, 06 Aug 2023 & \\textbf{ Prob (F-statistic):} & 5.08e-88 \\\\\n", - "\\textbf{Time:} & 17:18:30 & \\textbf{ Log-Likelihood: } & -1641.5 \\\\\n", + "\\textbf{Date:} & Sun, 20 Aug 2023 & \\textbf{ Prob (F-statistic):} & 5.08e-88 \\\\\n", + "\\textbf{Time:} & 19:28:54 & \\textbf{ Log-Likelihood: } & -1641.5 \\\\\n", "\\textbf{No. Observations:} & 506 & \\textbf{ AIC: } & 3287. \\\\\n", "\\textbf{Df Residuals:} & 504 & \\textbf{ BIC: } & 3295. \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -1088,8 +1087,8 @@ "Dep. Variable: medv R-squared: 0.544\n", "Model: OLS Adj. R-squared: 0.543\n", "Method: Least Squares F-statistic: 601.6\n", - "Date: Sun, 06 Aug 2023 Prob (F-statistic): 5.08e-88\n", - "Time: 17:18:30 Log-Likelihood: -1641.5\n", + "Date: Sun, 20 Aug 2023 Prob (F-statistic): 5.08e-88\n", + "Time: 19:28:54 Log-Likelihood: -1641.5\n", "No. Observations: 506 AIC: 3287.\n", "Df Residuals: 504 BIC: 3295.\n", "Df Model: 1 \n", @@ -1122,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "9a367738", + "id": "cbadf6fb", "metadata": {}, "source": [ "The fitted coefficients can also be retrieved as the\n", @@ -1132,13 +1131,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "a0edf555", + "id": "6d0f4c3a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.795155Z", - "iopub.status.busy": "2023-08-07T00:18:30.795070Z", - "iopub.status.idle": "2023-08-07T00:18:30.797528Z", - "shell.execute_reply": "2023-08-07T00:18:30.797283Z" + "iopub.execute_input": "2023-08-21T02:28:54.343317Z", + "iopub.status.busy": "2023-08-21T02:28:54.343206Z", + "iopub.status.idle": "2023-08-21T02:28:54.345604Z", + "shell.execute_reply": "2023-08-21T02:28:54.345339Z" }, "lines_to_next_cell": 2 }, @@ -1162,7 +1161,7 @@ }, { "cell_type": "markdown", - "id": "4472913f", + "id": "f97d477a", "metadata": {}, "source": [ "The `get_prediction()` method can be used to obtain predictions, and produce confidence intervals and\n", @@ -1175,13 +1174,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "fdc5a3f3", + "id": "132ffded", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.798957Z", - "iopub.status.busy": "2023-08-07T00:18:30.798873Z", - "iopub.status.idle": "2023-08-07T00:18:30.802562Z", - "shell.execute_reply": "2023-08-07T00:18:30.802313Z" + "iopub.execute_input": "2023-08-21T02:28:54.347097Z", + "iopub.status.busy": "2023-08-21T02:28:54.346984Z", + "iopub.status.idle": "2023-08-21T02:28:54.350802Z", + "shell.execute_reply": "2023-08-21T02:28:54.350563Z" } }, "outputs": [ @@ -1250,7 +1249,7 @@ }, { "cell_type": "markdown", - "id": "590d6b7d", + "id": "e99b90dc", "metadata": {}, "source": [ "Next we compute the predictions at `newX`, and view them by extracting the `predicted_mean` attribute." @@ -1259,13 +1258,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "2c6acbf0", + "id": "b654a050", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.803979Z", - "iopub.status.busy": "2023-08-07T00:18:30.803903Z", - "iopub.status.idle": "2023-08-07T00:18:30.806278Z", - "shell.execute_reply": "2023-08-07T00:18:30.806017Z" + "iopub.execute_input": "2023-08-21T02:28:54.352369Z", + "iopub.status.busy": "2023-08-21T02:28:54.352275Z", + "iopub.status.idle": "2023-08-21T02:28:54.354743Z", + "shell.execute_reply": "2023-08-21T02:28:54.354502Z" }, "lines_to_next_cell": 0 }, @@ -1288,7 +1287,7 @@ }, { "cell_type": "markdown", - "id": "f2d7e037", + "id": "cca6cef5", "metadata": {}, "source": [ "We can produce confidence intervals for the predicted values." @@ -1297,13 +1296,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "c472ef33", + "id": "148ed59d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.807645Z", - "iopub.status.busy": "2023-08-07T00:18:30.807570Z", - "iopub.status.idle": "2023-08-07T00:18:30.809883Z", - "shell.execute_reply": "2023-08-07T00:18:30.809646Z" + "iopub.execute_input": "2023-08-21T02:28:54.356140Z", + "iopub.status.busy": "2023-08-21T02:28:54.356063Z", + "iopub.status.idle": "2023-08-21T02:28:54.358315Z", + "shell.execute_reply": "2023-08-21T02:28:54.358059Z" }, "lines_to_next_cell": 0 }, @@ -1327,7 +1326,7 @@ }, { "cell_type": "markdown", - "id": "dfabf8a7", + "id": "c217e5e2", "metadata": {}, "source": [ "Prediction intervals are computing by setting `obs=True`:" @@ -1336,13 +1335,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "3e2ffc7a", + "id": "d9d7b844", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.811226Z", - "iopub.status.busy": "2023-08-07T00:18:30.811154Z", - "iopub.status.idle": "2023-08-07T00:18:30.813455Z", - "shell.execute_reply": "2023-08-07T00:18:30.813210Z" + "iopub.execute_input": "2023-08-21T02:28:54.359757Z", + "iopub.status.busy": "2023-08-21T02:28:54.359678Z", + "iopub.status.idle": "2023-08-21T02:28:54.362165Z", + "shell.execute_reply": "2023-08-21T02:28:54.361908Z" }, "lines_to_next_cell": 0 }, @@ -1366,7 +1365,7 @@ }, { "cell_type": "markdown", - "id": "65463c75", + "id": "a26d3660", "metadata": {}, "source": [ " For instance, the 95% confidence interval associated with an\n", @@ -1377,14 +1376,14 @@ "10), but the latter are substantially wider.\n", "\n", "Next we will plot `medv` and `lstat` \n", - "using `DataFrame.plot.scatter()`, \n", + "using `DataFrame.plot.scatter()`, \\definelongblankMR{plot.scatter()}{plot.slashslashscatter()}\n", "and wish to\n", "add the regression line to the resulting plot." ] }, { "cell_type": "markdown", - "id": "f699e856", + "id": "50db5bf6", "metadata": {}, "source": [ "### Defining Functions\n", @@ -1396,13 +1395,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "4e56a1d3", + "id": "79de2913", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.814985Z", - "iopub.status.busy": "2023-08-07T00:18:30.814910Z", - "iopub.status.idle": "2023-08-07T00:18:30.816725Z", - "shell.execute_reply": "2023-08-07T00:18:30.816494Z" + "iopub.execute_input": "2023-08-21T02:28:54.363741Z", + "iopub.status.busy": "2023-08-21T02:28:54.363634Z", + "iopub.status.idle": "2023-08-21T02:28:54.365531Z", + "shell.execute_reply": "2023-08-21T02:28:54.365292Z" }, "lines_to_next_cell": 0 }, @@ -1417,7 +1416,7 @@ }, { "cell_type": "markdown", - "id": "e0a0dce5", + "id": "f66dd38d", "metadata": {}, "source": [ " A few things are illustrated above. First we see the syntax for defining a function:\n", @@ -1430,13 +1429,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "7f43ffe7", + "id": "a9b843c7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.818143Z", - "iopub.status.busy": "2023-08-07T00:18:30.818041Z", - "iopub.status.idle": "2023-08-07T00:18:30.819998Z", - "shell.execute_reply": "2023-08-07T00:18:30.819773Z" + "iopub.execute_input": "2023-08-21T02:28:54.366874Z", + "iopub.status.busy": "2023-08-21T02:28:54.366784Z", + "iopub.status.idle": "2023-08-21T02:28:54.368713Z", + "shell.execute_reply": "2023-08-21T02:28:54.368464Z" }, "lines_to_next_cell": 0 }, @@ -1451,7 +1450,7 @@ }, { "cell_type": "markdown", - "id": "4f4981fa", + "id": "25ba4401", "metadata": {}, "source": [ "The addition of `*args` allows any number of\n", @@ -1470,13 +1469,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "3f7b67c9", + "id": "7e116800", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.821345Z", - "iopub.status.busy": "2023-08-07T00:18:30.821254Z", - "iopub.status.idle": "2023-08-07T00:18:30.930375Z", - "shell.execute_reply": "2023-08-07T00:18:30.929550Z" + "iopub.execute_input": "2023-08-21T02:28:54.370163Z", + "iopub.status.busy": "2023-08-21T02:28:54.370073Z", + "iopub.status.idle": "2023-08-21T02:28:54.466696Z", + "shell.execute_reply": "2023-08-21T02:28:54.466338Z" }, "lines_to_next_cell": 0 }, @@ -1503,7 +1502,7 @@ }, { "cell_type": "markdown", - "id": "50d7066f", + "id": "9a3e8b26", "metadata": {}, "source": [ "Thus, the final call to `ax.plot()` is `ax.plot(xlim, ylim, 'r--', linewidth=3)`.\n", @@ -1517,7 +1516,7 @@ }, { "cell_type": "markdown", - "id": "82ab1913", + "id": "1d78b981", "metadata": {}, "source": [ "Next we examine some diagnostic plots, several of which were discussed\n", @@ -1534,13 +1533,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "b35a2fd3", + "id": "b524399e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:30.934324Z", - "iopub.status.busy": "2023-08-07T00:18:30.934035Z", - "iopub.status.idle": "2023-08-07T00:18:31.102742Z", - "shell.execute_reply": "2023-08-07T00:18:31.101921Z" + "iopub.execute_input": "2023-08-21T02:28:54.468561Z", + "iopub.status.busy": "2023-08-21T02:28:54.468356Z", + "iopub.status.idle": "2023-08-21T02:28:54.566644Z", + "shell.execute_reply": "2023-08-21T02:28:54.566337Z" }, "lines_to_next_cell": 0 }, @@ -1566,7 +1565,7 @@ }, { "cell_type": "markdown", - "id": "4deb547b", + "id": "590a8be9", "metadata": {}, "source": [ " We add a horizontal line at 0 for reference using the\n", @@ -1582,13 +1581,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "82673b80", + "id": "8c95b6b0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.106724Z", - "iopub.status.busy": "2023-08-07T00:18:31.106292Z", - "iopub.status.idle": "2023-08-07T00:18:31.200890Z", - "shell.execute_reply": "2023-08-07T00:18:31.200597Z" + "iopub.execute_input": "2023-08-21T02:28:54.568423Z", + "iopub.status.busy": "2023-08-21T02:28:54.568307Z", + "iopub.status.idle": "2023-08-21T02:28:54.658553Z", + "shell.execute_reply": "2023-08-21T02:28:54.658267Z" }, "lines_to_next_cell": 0 }, @@ -1625,7 +1624,7 @@ }, { "cell_type": "markdown", - "id": "7eb69d75", + "id": "7712b1e1", "metadata": {}, "source": [ " The `np.argmax()` function identifies the index of the largest element of an array, optionally computed over an axis of the array.\n", @@ -1635,7 +1634,7 @@ }, { "cell_type": "markdown", - "id": "94c0a8c1", + "id": "c17dacc1", "metadata": {}, "source": [ "## Multiple Linear Regression\n", @@ -1650,13 +1649,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "54596dc4", + "id": "04ed8362", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.202583Z", - "iopub.status.busy": "2023-08-07T00:18:31.202480Z", - "iopub.status.idle": "2023-08-07T00:18:31.212391Z", - "shell.execute_reply": "2023-08-07T00:18:31.212135Z" + "iopub.execute_input": "2023-08-21T02:28:54.660197Z", + "iopub.status.busy": "2023-08-21T02:28:54.660075Z", + "iopub.status.idle": "2023-08-21T02:28:54.672237Z", + "shell.execute_reply": "2023-08-21T02:28:54.671994Z" }, "lines_to_next_cell": 0 }, @@ -1735,26 +1734,26 @@ }, { "cell_type": "markdown", - "id": "88450596", + "id": "3af076fd", "metadata": {}, "source": [ "Notice how we have compacted the first line into a succinct expression describing the construction of `X`.\n", "\n", "The `Boston` data set contains 12 variables, and so it would be cumbersome\n", "to have to type all of these in order to perform a regression using all of the predictors.\n", - "Instead, we can use the following short-hand:" + "Instead, we can use the following short-hand:\\definelongblankMR{columns.drop()}{columns.slashslashdrop()}" ] }, { "cell_type": "code", "execution_count": 26, - "id": "75c78238", + "id": "6483e190", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.214024Z", - "iopub.status.busy": "2023-08-07T00:18:31.213941Z", - "iopub.status.idle": "2023-08-07T00:18:31.216399Z", - "shell.execute_reply": "2023-08-07T00:18:31.216121Z" + "iopub.execute_input": "2023-08-21T02:28:54.673721Z", + "iopub.status.busy": "2023-08-21T02:28:54.673628Z", + "iopub.status.idle": "2023-08-21T02:28:54.676304Z", + "shell.execute_reply": "2023-08-21T02:28:54.675993Z" } }, "outputs": [ @@ -1778,7 +1777,7 @@ }, { "cell_type": "markdown", - "id": "653715b5", + "id": "b79a6810", "metadata": {}, "source": [ "We can now fit the model with all the variables in `terms` using\n", @@ -1788,13 +1787,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "f14b9e1a", + "id": "99a6f9d0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.217829Z", - "iopub.status.busy": "2023-08-07T00:18:31.217726Z", - "iopub.status.idle": "2023-08-07T00:18:31.232053Z", - "shell.execute_reply": "2023-08-07T00:18:31.231736Z" + "iopub.execute_input": "2023-08-21T02:28:54.677828Z", + "iopub.status.busy": "2023-08-21T02:28:54.677709Z", + "iopub.status.idle": "2023-08-21T02:28:54.696704Z", + "shell.execute_reply": "2023-08-21T02:28:54.696381Z" } }, "outputs": [ @@ -1952,7 +1951,7 @@ }, { "cell_type": "markdown", - "id": "26acd5ab", + "id": "719d42ae", "metadata": {}, "source": [ "What if we would like to perform a regression using all of the variables but one? For\n", @@ -1964,13 +1963,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "0a2714b1", + "id": "78cf55d0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.233879Z", - "iopub.status.busy": "2023-08-07T00:18:31.233764Z", - "iopub.status.idle": "2023-08-07T00:18:31.247573Z", - "shell.execute_reply": "2023-08-07T00:18:31.247300Z" + "iopub.execute_input": "2023-08-21T02:28:54.698261Z", + "iopub.status.busy": "2023-08-21T02:28:54.698177Z", + "iopub.status.idle": "2023-08-21T02:28:54.716362Z", + "shell.execute_reply": "2023-08-21T02:28:54.716082Z" } }, "outputs": [ @@ -2120,7 +2119,7 @@ }, { "cell_type": "markdown", - "id": "28538920", + "id": "526a80bc", "metadata": {}, "source": [ "## Multivariate Goodness of Fit\n", @@ -2152,13 +2151,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "961c9128", + "id": "902f6474", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.249268Z", - "iopub.status.busy": "2023-08-07T00:18:31.249149Z", - "iopub.status.idle": "2023-08-07T00:18:31.256436Z", - "shell.execute_reply": "2023-08-07T00:18:31.256170Z" + "iopub.execute_input": "2023-08-21T02:28:54.718076Z", + "iopub.status.busy": "2023-08-21T02:28:54.717951Z", + "iopub.status.idle": "2023-08-21T02:28:54.725146Z", + "shell.execute_reply": "2023-08-21T02:28:54.724859Z" }, "lines_to_next_cell": 0 }, @@ -2271,7 +2270,7 @@ }, { "cell_type": "markdown", - "id": "d052f0ab", + "id": "2a1567ea", "metadata": {}, "source": [ "The function `VIF()` takes two arguments: a dataframe or array,\n", @@ -2284,13 +2283,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "4886f9e9", + "id": "ea1c88e9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.257965Z", - "iopub.status.busy": "2023-08-07T00:18:31.257856Z", - "iopub.status.idle": "2023-08-07T00:18:31.262942Z", - "shell.execute_reply": "2023-08-07T00:18:31.262707Z" + "iopub.execute_input": "2023-08-21T02:28:54.726658Z", + "iopub.status.busy": "2023-08-21T02:28:54.726565Z", + "iopub.status.idle": "2023-08-21T02:28:54.731850Z", + "shell.execute_reply": "2023-08-21T02:28:54.731595Z" }, "lines_to_next_cell": 0 }, @@ -2303,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "fd3732d5", + "id": "18fb24e5", "metadata": {}, "source": [ "List comprehension allows us to perform such repetitive operations in a more straightforward way.\n", @@ -2317,13 +2316,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "b54d2da1", + "id": "e9ff159c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.264400Z", - "iopub.status.busy": "2023-08-07T00:18:31.264319Z", - "iopub.status.idle": "2023-08-07T00:18:31.274870Z", - "shell.execute_reply": "2023-08-07T00:18:31.274588Z" + "iopub.execute_input": "2023-08-21T02:28:54.733388Z", + "iopub.status.busy": "2023-08-21T02:28:54.733294Z", + "iopub.status.idle": "2023-08-21T02:28:54.747346Z", + "shell.execute_reply": "2023-08-21T02:28:54.747067Z" }, "lines_to_next_cell": 2 }, @@ -2411,7 +2410,7 @@ }, { "cell_type": "markdown", - "id": "c72c4846", + "id": "92604d18", "metadata": {}, "source": [ "## Non-linear Transformations of the Predictors\n", @@ -2425,13 +2424,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "1b71633a", + "id": "c98f54b2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.276448Z", - "iopub.status.busy": "2023-08-07T00:18:31.276353Z", - "iopub.status.idle": "2023-08-07T00:18:31.287456Z", - "shell.execute_reply": "2023-08-07T00:18:31.287173Z" + "iopub.execute_input": "2023-08-21T02:28:54.748856Z", + "iopub.status.busy": "2023-08-21T02:28:54.748774Z", + "iopub.status.idle": "2023-08-21T02:28:54.763149Z", + "shell.execute_reply": "2023-08-21T02:28:54.762853Z" }, "lines_to_next_cell": 0 }, @@ -2518,7 +2517,7 @@ }, { "cell_type": "markdown", - "id": "9f32c813", + "id": "ccc11d8c", "metadata": {}, "source": [ "The effectively zero *p*-value associated with the quadratic term\n", @@ -2543,13 +2542,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "6d30a306", + "id": "53065cac", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.289067Z", - "iopub.status.busy": "2023-08-07T00:18:31.288970Z", - "iopub.status.idle": "2023-08-07T00:18:31.294816Z", - "shell.execute_reply": "2023-08-07T00:18:31.294545Z" + "iopub.execute_input": "2023-08-21T02:28:54.764924Z", + "iopub.status.busy": "2023-08-21T02:28:54.764796Z", + "iopub.status.idle": "2023-08-21T02:28:54.770846Z", + "shell.execute_reply": "2023-08-21T02:28:54.770578Z" }, "lines_to_next_cell": 0 }, @@ -2623,7 +2622,7 @@ }, { "cell_type": "markdown", - "id": "bd0155d4", + "id": "c0696dc2", "metadata": {}, "source": [ "Here `results1` represents the linear submodel containing\n", @@ -2653,13 +2652,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "9a5ec13f", + "id": "8c654809", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.296264Z", - "iopub.status.busy": "2023-08-07T00:18:31.296175Z", - "iopub.status.idle": "2023-08-07T00:18:31.396371Z", - "shell.execute_reply": "2023-08-07T00:18:31.396036Z" + "iopub.execute_input": "2023-08-21T02:28:54.772408Z", + "iopub.status.busy": "2023-08-21T02:28:54.772296Z", + "iopub.status.idle": "2023-08-21T02:28:54.872389Z", + "shell.execute_reply": "2023-08-21T02:28:54.872086Z" }, "lines_to_next_cell": 0 }, @@ -2667,7 +2666,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 34, @@ -2695,7 +2694,7 @@ }, { "cell_type": "markdown", - "id": "88272f6f", + "id": "f79b542d", "metadata": {}, "source": [ "We see that when the quadratic term is included in the model,\n", @@ -2706,7 +2705,7 @@ }, { "cell_type": "markdown", - "id": "e1a34084", + "id": "6123a191", "metadata": {}, "source": [ "## Qualitative Predictors\n", @@ -2719,13 +2718,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "09bbc0c6", + "id": "2182f0ec", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.398199Z", - "iopub.status.busy": "2023-08-07T00:18:31.398079Z", - "iopub.status.idle": "2023-08-07T00:18:31.403054Z", - "shell.execute_reply": "2023-08-07T00:18:31.402775Z" + "iopub.execute_input": "2023-08-21T02:28:54.874000Z", + "iopub.status.busy": "2023-08-21T02:28:54.873893Z", + "iopub.status.idle": "2023-08-21T02:28:54.879085Z", + "shell.execute_reply": "2023-08-21T02:28:54.878817Z" }, "lines_to_next_cell": 0 }, @@ -2750,7 +2749,7 @@ }, { "cell_type": "markdown", - "id": "1a882e65", + "id": "66672403", "metadata": {}, "source": [ "The `Carseats` \n", @@ -2770,13 +2769,13 @@ { "cell_type": "code", "execution_count": 36, - "id": "2e1da1fa", + "id": "d614fdcb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:31.404630Z", - "iopub.status.busy": "2023-08-07T00:18:31.404535Z", - "iopub.status.idle": "2023-08-07T00:18:31.424741Z", - "shell.execute_reply": "2023-08-07T00:18:31.424459Z" + "iopub.execute_input": "2023-08-21T02:28:54.880583Z", + "iopub.status.busy": "2023-08-21T02:28:54.880499Z", + "iopub.status.idle": "2023-08-21T02:28:54.907341Z", + "shell.execute_reply": "2023-08-21T02:28:54.907079Z" }, "lines_to_next_cell": 0 }, @@ -2946,7 +2945,7 @@ }, { "cell_type": "markdown", - "id": "79d9127c", + "id": "2402763b", "metadata": {}, "source": [ "In the first line above, we made `allvars` a list, so that we\n", diff --git a/Ch04-classification-lab.Rmd b/Ch04-classification-lab.Rmd index 6266d03..d3d8eec 100644 --- a/Ch04-classification-lab.Rmd +++ b/Ch04-classification-lab.Rmd @@ -807,7 +807,7 @@ feature_std.std() ``` -Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 103. +Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 200. In this case it does not matter, as long as the variables are all on the same scale. Using the function `train_test_split()` we now split the observations into a test set, diff --git a/Ch04-classification-lab.ipynb b/Ch04-classification-lab.ipynb index 90fb9c8..0ab61a0 100644 --- a/Ch04-classification-lab.ipynb +++ b/Ch04-classification-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "cb3c07d1", + "id": "5443fa92", "metadata": {}, "source": [ "\n", @@ -13,7 +13,7 @@ }, { "cell_type": "markdown", - "id": "daef3e2e", + "id": "3fc2dfec", "metadata": {}, "source": [ "# Lab: Logistic Regression, LDA, QDA, and KNN\n" @@ -21,7 +21,7 @@ }, { "cell_type": "markdown", - "id": "3cb93478", + "id": "0c96d561", "metadata": {}, "source": [ "## The Stock Market Data\n", @@ -43,13 +43,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "7b8d78b9", + "id": "95d28c33", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:32.688752Z", - "iopub.status.busy": "2023-08-07T00:18:32.688456Z", - "iopub.status.idle": "2023-08-07T00:18:33.723127Z", - "shell.execute_reply": "2023-08-07T00:18:33.722505Z" + "iopub.execute_input": "2023-08-21T02:28:56.229892Z", + "iopub.status.busy": "2023-08-21T02:28:56.229672Z", + "iopub.status.idle": "2023-08-21T02:28:57.250402Z", + "shell.execute_reply": "2023-08-21T02:28:57.249963Z" } }, "outputs": [], @@ -65,7 +65,7 @@ }, { "cell_type": "markdown", - "id": "f420141c", + "id": "0c887b44", "metadata": {}, "source": [ "We also collect together the new imports needed for this lab." @@ -74,13 +74,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "b5938862", + "id": "f7fb5f2a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:33.725437Z", - "iopub.status.busy": "2023-08-07T00:18:33.725139Z", - "iopub.status.idle": "2023-08-07T00:18:33.743611Z", - "shell.execute_reply": "2023-08-07T00:18:33.743322Z" + "iopub.execute_input": "2023-08-21T02:28:57.252390Z", + "iopub.status.busy": "2023-08-21T02:28:57.252224Z", + "iopub.status.idle": "2023-08-21T02:28:57.272851Z", + "shell.execute_reply": "2023-08-21T02:28:57.272588Z" }, "lines_to_next_cell": 2 }, @@ -100,7 +100,7 @@ }, { "cell_type": "markdown", - "id": "34413b1c", + "id": "f5fd2bfd", "metadata": {}, "source": [ "Now we are ready to load the `Smarket` data." @@ -109,13 +109,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "e9fdf040", + "id": "7845390b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:33.745195Z", - "iopub.status.busy": "2023-08-07T00:18:33.745084Z", - "iopub.status.idle": "2023-08-07T00:18:33.757813Z", - "shell.execute_reply": "2023-08-07T00:18:33.757521Z" + "iopub.execute_input": "2023-08-21T02:28:57.274355Z", + "iopub.status.busy": "2023-08-21T02:28:57.274258Z", + "iopub.status.idle": "2023-08-21T02:28:57.285160Z", + "shell.execute_reply": "2023-08-21T02:28:57.284894Z" }, "lines_to_next_cell": 0 }, @@ -319,7 +319,7 @@ }, { "cell_type": "markdown", - "id": "d3533960", + "id": "6b585d9b", "metadata": {}, "source": [ "This gives a truncated listing of the data.\n", @@ -329,13 +329,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "69d5cfae", + "id": "a92e287a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:33.759381Z", - "iopub.status.busy": "2023-08-07T00:18:33.759278Z", - "iopub.status.idle": "2023-08-07T00:18:33.761642Z", - "shell.execute_reply": "2023-08-07T00:18:33.761397Z" + "iopub.execute_input": "2023-08-21T02:28:57.286715Z", + "iopub.status.busy": "2023-08-21T02:28:57.286616Z", + "iopub.status.idle": "2023-08-21T02:28:57.288883Z", + "shell.execute_reply": "2023-08-21T02:28:57.288648Z" }, "lines_to_next_cell": 0 }, @@ -359,7 +359,7 @@ }, { "cell_type": "markdown", - "id": "c1e0cc8c", + "id": "31ec6135", "metadata": {}, "source": [ "We compute the correlation matrix using the `corr()` method\n", @@ -373,13 +373,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "a4c0648e", + "id": "96bb1e00", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:33.763041Z", - "iopub.status.busy": "2023-08-07T00:18:33.762950Z", - "iopub.status.idle": "2023-08-07T00:18:33.767711Z", - "shell.execute_reply": "2023-08-07T00:18:33.767453Z" + "iopub.execute_input": "2023-08-21T02:28:57.290357Z", + "iopub.status.busy": "2023-08-21T02:28:57.290259Z", + "iopub.status.idle": "2023-08-21T02:28:57.295352Z", + "shell.execute_reply": "2023-08-21T02:28:57.295083Z" }, "lines_to_next_cell": 0 }, @@ -541,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "c68bb37a", + "id": "9ff113ca", "metadata": {}, "source": [ "As one would expect, the correlations between the lagged return variables and\n", @@ -554,13 +554,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "cd40274b", + "id": "4ddb96ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:33.769148Z", - "iopub.status.busy": "2023-08-07T00:18:33.769072Z", - "iopub.status.idle": "2023-08-07T00:18:33.882743Z", - "shell.execute_reply": "2023-08-07T00:18:33.880710Z" + "iopub.execute_input": "2023-08-21T02:28:57.296784Z", + "iopub.status.busy": "2023-08-21T02:28:57.296683Z", + "iopub.status.idle": "2023-08-21T02:28:57.392956Z", + "shell.execute_reply": "2023-08-21T02:28:57.392526Z" }, "lines_to_next_cell": 2 }, @@ -582,7 +582,7 @@ }, { "cell_type": "markdown", - "id": "685b96e8", + "id": "c2fe10a0", "metadata": {}, "source": [ "## Logistic Regression\n", @@ -601,13 +601,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "35b8bffe", + "id": "df59bcac", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:33.886763Z", - "iopub.status.busy": "2023-08-07T00:18:33.886512Z", - "iopub.status.idle": "2023-08-07T00:18:34.002815Z", - "shell.execute_reply": "2023-08-07T00:18:34.002006Z" + "iopub.execute_input": "2023-08-21T02:28:57.394870Z", + "iopub.status.busy": "2023-08-21T02:28:57.394721Z", + "iopub.status.idle": "2023-08-21T02:28:57.461746Z", + "shell.execute_reply": "2023-08-21T02:28:57.461390Z" }, "lines_to_next_cell": 0 }, @@ -723,7 +723,7 @@ }, { "cell_type": "markdown", - "id": "68cca865", + "id": "cc5b69fc", "metadata": {}, "source": [ "The smallest *p*-value here is associated with `Lag1`. The\n", @@ -741,13 +741,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "4f9c5324", + "id": "f45f26de", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.007435Z", - "iopub.status.busy": "2023-08-07T00:18:34.006910Z", - "iopub.status.idle": "2023-08-07T00:18:34.014901Z", - "shell.execute_reply": "2023-08-07T00:18:34.014079Z" + "iopub.execute_input": "2023-08-21T02:28:57.463719Z", + "iopub.status.busy": "2023-08-21T02:28:57.463515Z", + "iopub.status.idle": "2023-08-21T02:28:57.466512Z", + "shell.execute_reply": "2023-08-21T02:28:57.466227Z" }, "lines_to_next_cell": 0 }, @@ -776,7 +776,7 @@ }, { "cell_type": "markdown", - "id": "681ca0cb", + "id": "56749219", "metadata": {}, "source": [ "Likewise we can use the\n", @@ -786,13 +786,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "75e44d01", + "id": "e9f38895", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.019225Z", - "iopub.status.busy": "2023-08-07T00:18:34.018883Z", - "iopub.status.idle": "2023-08-07T00:18:34.025804Z", - "shell.execute_reply": "2023-08-07T00:18:34.025086Z" + "iopub.execute_input": "2023-08-21T02:28:57.468254Z", + "iopub.status.busy": "2023-08-21T02:28:57.468113Z", + "iopub.status.idle": "2023-08-21T02:28:57.470717Z", + "shell.execute_reply": "2023-08-21T02:28:57.470403Z" } }, "outputs": [ @@ -820,7 +820,7 @@ }, { "cell_type": "markdown", - "id": "d4fd87bf", + "id": "b5f86e0b", "metadata": {}, "source": [ "The `predict()` method of `results` can be used to predict the\n", @@ -837,13 +837,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "40164af5", + "id": "4f20356d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.029989Z", - "iopub.status.busy": "2023-08-07T00:18:34.029690Z", - "iopub.status.idle": "2023-08-07T00:18:34.036791Z", - "shell.execute_reply": "2023-08-07T00:18:34.036138Z" + "iopub.execute_input": "2023-08-21T02:28:57.472719Z", + "iopub.status.busy": "2023-08-21T02:28:57.472578Z", + "iopub.status.idle": "2023-08-21T02:28:57.475369Z", + "shell.execute_reply": "2023-08-21T02:28:57.475043Z" }, "lines_to_next_cell": 0 }, @@ -867,7 +867,7 @@ }, { "cell_type": "markdown", - "id": "94607ba9", + "id": "4aab2080", "metadata": {}, "source": [ "In order to make a prediction as to whether the market will go up or\n", @@ -881,13 +881,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "daf20de2", + "id": "152b3063", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.040976Z", - "iopub.status.busy": "2023-08-07T00:18:34.040685Z", - "iopub.status.idle": "2023-08-07T00:18:34.046531Z", - "shell.execute_reply": "2023-08-07T00:18:34.045877Z" + "iopub.execute_input": "2023-08-21T02:28:57.477166Z", + "iopub.status.busy": "2023-08-21T02:28:57.477052Z", + "iopub.status.idle": "2023-08-21T02:28:57.479161Z", + "shell.execute_reply": "2023-08-21T02:28:57.478803Z" }, "lines_to_next_cell": 0 }, @@ -899,7 +899,7 @@ }, { "cell_type": "markdown", - "id": "085aead4", + "id": "a825abf5", "metadata": {}, "source": [ "The `confusion_table()`\n", @@ -914,13 +914,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "71f4a40c", + "id": "0f89f7ae", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.050481Z", - "iopub.status.busy": "2023-08-07T00:18:34.050167Z", - "iopub.status.idle": "2023-08-07T00:18:34.059868Z", - "shell.execute_reply": "2023-08-07T00:18:34.059588Z" + "iopub.execute_input": "2023-08-21T02:28:57.480867Z", + "iopub.status.busy": "2023-08-21T02:28:57.480737Z", + "iopub.status.idle": "2023-08-21T02:28:57.487133Z", + "shell.execute_reply": "2023-08-21T02:28:57.486778Z" } }, "outputs": [ @@ -987,7 +987,7 @@ }, { "cell_type": "markdown", - "id": "3a338f26", + "id": "9e9ff255", "metadata": {}, "source": [ "The diagonal elements of the confusion matrix indicate correct\n", @@ -1003,13 +1003,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "78b2946b", + "id": "d55dd7ec", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.061456Z", - "iopub.status.busy": "2023-08-07T00:18:34.061336Z", - "iopub.status.idle": "2023-08-07T00:18:34.064074Z", - "shell.execute_reply": "2023-08-07T00:18:34.063814Z" + "iopub.execute_input": "2023-08-21T02:28:57.488873Z", + "iopub.status.busy": "2023-08-21T02:28:57.488756Z", + "iopub.status.idle": "2023-08-21T02:28:57.491458Z", + "shell.execute_reply": "2023-08-21T02:28:57.491162Z" }, "lines_to_next_cell": 0 }, @@ -1031,7 +1031,7 @@ }, { "cell_type": "markdown", - "id": "54962226", + "id": "d73690be", "metadata": {}, "source": [ "\n" @@ -1039,7 +1039,7 @@ }, { "cell_type": "markdown", - "id": "aa9be981", + "id": "3d8958eb", "metadata": {}, "source": [ "At first glance, it appears that the logistic regression model is\n", @@ -1066,13 +1066,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "e8a4d12e", + "id": "b998a060", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.065518Z", - "iopub.status.busy": "2023-08-07T00:18:34.065416Z", - "iopub.status.idle": "2023-08-07T00:18:34.068254Z", - "shell.execute_reply": "2023-08-07T00:18:34.067978Z" + "iopub.execute_input": "2023-08-21T02:28:57.493306Z", + "iopub.status.busy": "2023-08-21T02:28:57.493174Z", + "iopub.status.idle": "2023-08-21T02:28:57.496464Z", + "shell.execute_reply": "2023-08-21T02:28:57.496107Z" }, "lines_to_next_cell": 2 }, @@ -1097,7 +1097,7 @@ }, { "cell_type": "markdown", - "id": "946fac68", + "id": "8c337c63", "metadata": {}, "source": [ "The object `train` is a vector of 1,250 elements, corresponding\n", @@ -1132,13 +1132,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "fd16bc2c", + "id": "814e34ce", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.069659Z", - "iopub.status.busy": "2023-08-07T00:18:34.069561Z", - "iopub.status.idle": "2023-08-07T00:18:34.074343Z", - "shell.execute_reply": "2023-08-07T00:18:34.074055Z" + "iopub.execute_input": "2023-08-21T02:28:57.498135Z", + "iopub.status.busy": "2023-08-21T02:28:57.498043Z", + "iopub.status.idle": "2023-08-21T02:28:57.502996Z", + "shell.execute_reply": "2023-08-21T02:28:57.502676Z" } }, "outputs": [], @@ -1154,7 +1154,7 @@ }, { "cell_type": "markdown", - "id": "12382c18", + "id": "312c605a", "metadata": {}, "source": [ "Notice that we have trained and tested our model on two completely\n", @@ -1169,13 +1169,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "aaa9d07c", + "id": "644823f9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.075820Z", - "iopub.status.busy": "2023-08-07T00:18:34.075734Z", - "iopub.status.idle": "2023-08-07T00:18:34.077644Z", - "shell.execute_reply": "2023-08-07T00:18:34.077397Z" + "iopub.execute_input": "2023-08-21T02:28:57.504874Z", + "iopub.status.busy": "2023-08-21T02:28:57.504743Z", + "iopub.status.idle": "2023-08-21T02:28:57.506879Z", + "shell.execute_reply": "2023-08-21T02:28:57.506558Z" }, "lines_to_next_cell": 0 }, @@ -1187,7 +1187,7 @@ }, { "cell_type": "markdown", - "id": "cc0d0c3a", + "id": "4c9f0ae0", "metadata": {}, "source": [ "Now we threshold the\n", @@ -1198,13 +1198,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "5f0b3282", + "id": "51217c85", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.079028Z", - "iopub.status.busy": "2023-08-07T00:18:34.078951Z", - "iopub.status.idle": "2023-08-07T00:18:34.083263Z", - "shell.execute_reply": "2023-08-07T00:18:34.083022Z" + "iopub.execute_input": "2023-08-21T02:28:57.508502Z", + "iopub.status.busy": "2023-08-21T02:28:57.508403Z", + "iopub.status.idle": "2023-08-21T02:28:57.512973Z", + "shell.execute_reply": "2023-08-21T02:28:57.512720Z" }, "lines_to_next_cell": 0 }, @@ -1274,7 +1274,7 @@ }, { "cell_type": "markdown", - "id": "71ca598e", + "id": "ff5f3218", "metadata": {}, "source": [ "The test accuracy is about 48% while the error rate is about 52%" @@ -1283,13 +1283,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "7f4e49c0", + "id": "a73446bf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.084664Z", - "iopub.status.busy": "2023-08-07T00:18:34.084577Z", - "iopub.status.idle": "2023-08-07T00:18:34.087059Z", - "shell.execute_reply": "2023-08-07T00:18:34.086816Z" + "iopub.execute_input": "2023-08-21T02:28:57.514459Z", + "iopub.status.busy": "2023-08-21T02:28:57.514377Z", + "iopub.status.idle": "2023-08-21T02:28:57.517011Z", + "shell.execute_reply": "2023-08-21T02:28:57.516758Z" }, "lines_to_next_cell": 2 }, @@ -1311,7 +1311,7 @@ }, { "cell_type": "markdown", - "id": "96d75735", + "id": "149949d9", "metadata": {}, "source": [ "The `!=` notation means *not equal to*, and so the last command\n", @@ -1341,13 +1341,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "9f94de4b", + "id": "8174767d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.088387Z", - "iopub.status.busy": "2023-08-07T00:18:34.088307Z", - "iopub.status.idle": "2023-08-07T00:18:34.096165Z", - "shell.execute_reply": "2023-08-07T00:18:34.095892Z" + "iopub.execute_input": "2023-08-21T02:28:57.518464Z", + "iopub.status.busy": "2023-08-21T02:28:57.518363Z", + "iopub.status.idle": "2023-08-21T02:28:57.528289Z", + "shell.execute_reply": "2023-08-21T02:28:57.527983Z" }, "lines_to_next_cell": 2 }, @@ -1425,7 +1425,7 @@ }, { "cell_type": "markdown", - "id": "1dd9ce3d", + "id": "9992d709", "metadata": {}, "source": [ "Let’s evaluate the overall accuracy as well as the accuracy within the days when\n", @@ -1435,13 +1435,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "69a4dd6b", + "id": "cba7e815", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.097583Z", - "iopub.status.busy": "2023-08-07T00:18:34.097501Z", - "iopub.status.idle": "2023-08-07T00:18:34.099616Z", - "shell.execute_reply": "2023-08-07T00:18:34.099375Z" + "iopub.execute_input": "2023-08-21T02:28:57.529915Z", + "iopub.status.busy": "2023-08-21T02:28:57.529812Z", + "iopub.status.idle": "2023-08-21T02:28:57.532108Z", + "shell.execute_reply": "2023-08-21T02:28:57.531849Z" } }, "outputs": [ @@ -1462,7 +1462,7 @@ }, { "cell_type": "markdown", - "id": "14cf6b55", + "id": "cfe9b256", "metadata": {}, "source": [ "Now the results appear to be a little better: 56% of the daily\n", @@ -1489,13 +1489,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "975fa3ca", + "id": "97993185", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.101046Z", - "iopub.status.busy": "2023-08-07T00:18:34.100966Z", - "iopub.status.idle": "2023-08-07T00:18:34.104502Z", - "shell.execute_reply": "2023-08-07T00:18:34.104254Z" + "iopub.execute_input": "2023-08-21T02:28:57.533565Z", + "iopub.status.busy": "2023-08-21T02:28:57.533462Z", + "iopub.status.idle": "2023-08-21T02:28:57.537458Z", + "shell.execute_reply": "2023-08-21T02:28:57.537180Z" }, "lines_to_next_cell": 2 }, @@ -1522,7 +1522,7 @@ }, { "cell_type": "markdown", - "id": "bdc2c0bb", + "id": "3b56915d", "metadata": {}, "source": [ "## Linear Discriminant Analysis" @@ -1530,7 +1530,7 @@ }, { "cell_type": "markdown", - "id": "41deab79", + "id": "5fd3e6e0", "metadata": {}, "source": [ "We begin by performing LDA on the `Smarket` data, using the function\n", @@ -1541,13 +1541,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "7237ffa7", + "id": "4bc774e9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.105969Z", - "iopub.status.busy": "2023-08-07T00:18:34.105892Z", - "iopub.status.idle": "2023-08-07T00:18:34.107535Z", - "shell.execute_reply": "2023-08-07T00:18:34.107296Z" + "iopub.execute_input": "2023-08-21T02:28:57.539054Z", + "iopub.status.busy": "2023-08-21T02:28:57.538952Z", + "iopub.status.idle": "2023-08-21T02:28:57.540857Z", + "shell.execute_reply": "2023-08-21T02:28:57.540537Z" } }, "outputs": [], @@ -1557,7 +1557,7 @@ }, { "cell_type": "markdown", - "id": "7289db40", + "id": "3d1a82d1", "metadata": {}, "source": [ "Since the `LDA` estimator automatically \n", @@ -1569,13 +1569,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "43a850eb", + "id": "8c6c0723", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.108941Z", - "iopub.status.busy": "2023-08-07T00:18:34.108865Z", - "iopub.status.idle": "2023-08-07T00:18:34.114955Z", - "shell.execute_reply": "2023-08-07T00:18:34.114708Z" + "iopub.execute_input": "2023-08-21T02:28:57.542568Z", + "iopub.status.busy": "2023-08-21T02:28:57.542481Z", + "iopub.status.idle": "2023-08-21T02:28:57.548927Z", + "shell.execute_reply": "2023-08-21T02:28:57.548581Z" }, "lines_to_next_cell": 0 }, @@ -1602,7 +1602,7 @@ }, { "cell_type": "markdown", - "id": "d3e0901f", + "id": "d4db3bde", "metadata": {}, "source": [ "Here we have used the list comprehensions introduced\n", @@ -1625,13 +1625,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "86b6cece", + "id": "cf8fd5ac", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.116386Z", - "iopub.status.busy": "2023-08-07T00:18:34.116305Z", - "iopub.status.idle": "2023-08-07T00:18:34.118455Z", - "shell.execute_reply": "2023-08-07T00:18:34.118209Z" + "iopub.execute_input": "2023-08-21T02:28:57.550601Z", + "iopub.status.busy": "2023-08-21T02:28:57.550496Z", + "iopub.status.idle": "2023-08-21T02:28:57.552737Z", + "shell.execute_reply": "2023-08-21T02:28:57.552471Z" }, "lines_to_next_cell": 2 }, @@ -1654,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "6d75a932", + "id": "bcd5c21f", "metadata": {}, "source": [ "The estimated prior probabilities are stored in the `priors_` attribute.\n", @@ -1666,13 +1666,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "9362c7c6", + "id": "bfd6b3f8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.119865Z", - "iopub.status.busy": "2023-08-07T00:18:34.119787Z", - "iopub.status.idle": "2023-08-07T00:18:34.121925Z", - "shell.execute_reply": "2023-08-07T00:18:34.121681Z" + "iopub.execute_input": "2023-08-21T02:28:57.554217Z", + "iopub.status.busy": "2023-08-21T02:28:57.554117Z", + "iopub.status.idle": "2023-08-21T02:28:57.556249Z", + "shell.execute_reply": "2023-08-21T02:28:57.555998Z" }, "lines_to_next_cell": 2 }, @@ -1694,7 +1694,7 @@ }, { "cell_type": "markdown", - "id": "61f77e19", + "id": "60fe91d9", "metadata": {}, "source": [ "The LDA output indicates that $\\hat\\pi_{Down}=0.492$ and\n", @@ -1704,13 +1704,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "e59858dc", + "id": "a1f75de4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.123314Z", - "iopub.status.busy": "2023-08-07T00:18:34.123233Z", - "iopub.status.idle": "2023-08-07T00:18:34.125369Z", - "shell.execute_reply": "2023-08-07T00:18:34.125137Z" + "iopub.execute_input": "2023-08-21T02:28:57.557716Z", + "iopub.status.busy": "2023-08-21T02:28:57.557623Z", + "iopub.status.idle": "2023-08-21T02:28:57.559886Z", + "shell.execute_reply": "2023-08-21T02:28:57.559582Z" }, "lines_to_next_cell": 2 }, @@ -1732,7 +1732,7 @@ }, { "cell_type": "markdown", - "id": "43a8fde1", + "id": "47e60686", "metadata": {}, "source": [ "The linear discriminant vectors can be found in the `scalings_` attribute:" @@ -1741,13 +1741,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "9b5f74e8", + "id": "82794178", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.126738Z", - "iopub.status.busy": "2023-08-07T00:18:34.126660Z", - "iopub.status.idle": "2023-08-07T00:18:34.128726Z", - "shell.execute_reply": "2023-08-07T00:18:34.128484Z" + "iopub.execute_input": "2023-08-21T02:28:57.561606Z", + "iopub.status.busy": "2023-08-21T02:28:57.561489Z", + "iopub.status.idle": "2023-08-21T02:28:57.563996Z", + "shell.execute_reply": "2023-08-21T02:28:57.563652Z" } }, "outputs": [ @@ -1769,7 +1769,7 @@ }, { "cell_type": "markdown", - "id": "4655809e", + "id": "ac6ad759", "metadata": {}, "source": [ "These values provide the linear combination of `Lag1` and `Lag2` that are used to form the LDA decision rule. In other words, these are the multipliers of the elements of $X=x$ in (4.24).\n", @@ -1779,13 +1779,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "fd8e6794", + "id": "9b571047", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.130043Z", - "iopub.status.busy": "2023-08-07T00:18:34.129971Z", - "iopub.status.idle": "2023-08-07T00:18:34.132079Z", - "shell.execute_reply": "2023-08-07T00:18:34.131833Z" + "iopub.execute_input": "2023-08-21T02:28:57.565689Z", + "iopub.status.busy": "2023-08-21T02:28:57.565573Z", + "iopub.status.idle": "2023-08-21T02:28:57.568080Z", + "shell.execute_reply": "2023-08-21T02:28:57.567748Z" } }, "outputs": [], @@ -1795,7 +1795,7 @@ }, { "cell_type": "markdown", - "id": "2d211f0a", + "id": "61522ffc", "metadata": {}, "source": [ "As we observed in our comparison of classification methods\n", @@ -1806,13 +1806,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "3bb63fb1", + "id": "60f3d13a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.133469Z", - "iopub.status.busy": "2023-08-07T00:18:34.133393Z", - "iopub.status.idle": "2023-08-07T00:18:34.137177Z", - "shell.execute_reply": "2023-08-07T00:18:34.136944Z" + "iopub.execute_input": "2023-08-21T02:28:57.569793Z", + "iopub.status.busy": "2023-08-21T02:28:57.569674Z", + "iopub.status.idle": "2023-08-21T02:28:57.574322Z", + "shell.execute_reply": "2023-08-21T02:28:57.573986Z" }, "lines_to_next_cell": 2 }, @@ -1880,7 +1880,7 @@ }, { "cell_type": "markdown", - "id": "8e31152b", + "id": "6b88f943", "metadata": {}, "source": [ "We can also estimate the\n", @@ -1893,13 +1893,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "2b91f491", + "id": "96e680d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.138619Z", - "iopub.status.busy": "2023-08-07T00:18:34.138537Z", - "iopub.status.idle": "2023-08-07T00:18:34.141314Z", - "shell.execute_reply": "2023-08-07T00:18:34.141072Z" + "iopub.execute_input": "2023-08-21T02:28:57.576103Z", + "iopub.status.busy": "2023-08-21T02:28:57.575991Z", + "iopub.status.idle": "2023-08-21T02:28:57.579068Z", + "shell.execute_reply": "2023-08-21T02:28:57.578805Z" }, "lines_to_next_cell": 2 }, @@ -1924,7 +1924,7 @@ }, { "cell_type": "markdown", - "id": "d3a76822", + "id": "d20f6d2b", "metadata": {}, "source": [ "Above, we used the `np.where()` function that\n", @@ -1937,13 +1937,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "3a085714", + "id": "b6695125", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.142668Z", - "iopub.status.busy": "2023-08-07T00:18:34.142591Z", - "iopub.status.idle": "2023-08-07T00:18:34.144963Z", - "shell.execute_reply": "2023-08-07T00:18:34.144712Z" + "iopub.execute_input": "2023-08-21T02:28:57.580746Z", + "iopub.status.busy": "2023-08-21T02:28:57.580639Z", + "iopub.status.idle": "2023-08-21T02:28:57.583327Z", + "shell.execute_reply": "2023-08-21T02:28:57.583073Z" }, "lines_to_next_cell": 2 }, @@ -1967,7 +1967,7 @@ }, { "cell_type": "markdown", - "id": "0aa9cbb3", + "id": "9661ed20", "metadata": {}, "source": [ "If we wanted to use a posterior probability threshold other than\n", @@ -1983,13 +1983,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "345fb8c6", + "id": "3f38a14e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.146325Z", - "iopub.status.busy": "2023-08-07T00:18:34.146248Z", - "iopub.status.idle": "2023-08-07T00:18:34.148323Z", - "shell.execute_reply": "2023-08-07T00:18:34.148102Z" + "iopub.execute_input": "2023-08-21T02:28:57.584834Z", + "iopub.status.busy": "2023-08-21T02:28:57.584748Z", + "iopub.status.idle": "2023-08-21T02:28:57.587140Z", + "shell.execute_reply": "2023-08-21T02:28:57.586884Z" } }, "outputs": [ @@ -2010,7 +2010,7 @@ }, { "cell_type": "markdown", - "id": "6798146c", + "id": "c12fe607", "metadata": {}, "source": [ "No days in 2005 meet that threshold! In fact, the greatest posterior\n", @@ -2034,7 +2034,7 @@ }, { "cell_type": "markdown", - "id": "58f2984f", + "id": "8a384f92", "metadata": {}, "source": [ "## Quadratic Discriminant Analysis\n", @@ -2048,13 +2048,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "22c6c2a3", + "id": "9b645803", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.149774Z", - "iopub.status.busy": "2023-08-07T00:18:34.149693Z", - "iopub.status.idle": "2023-08-07T00:18:34.153308Z", - "shell.execute_reply": "2023-08-07T00:18:34.153071Z" + "iopub.execute_input": "2023-08-21T02:28:57.588684Z", + "iopub.status.busy": "2023-08-21T02:28:57.588575Z", + "iopub.status.idle": "2023-08-21T02:28:57.592433Z", + "shell.execute_reply": "2023-08-21T02:28:57.592165Z" } }, "outputs": [ @@ -2079,7 +2079,7 @@ }, { "cell_type": "markdown", - "id": "fba0bb3a", + "id": "0c595cd6", "metadata": {}, "source": [ "The `QDA()` function will again compute `means_` and `priors_`." @@ -2088,13 +2088,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "992a790a", + "id": "abfae544", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.154710Z", - "iopub.status.busy": "2023-08-07T00:18:34.154631Z", - "iopub.status.idle": "2023-08-07T00:18:34.156853Z", - "shell.execute_reply": "2023-08-07T00:18:34.156611Z" + "iopub.execute_input": "2023-08-21T02:28:57.593864Z", + "iopub.status.busy": "2023-08-21T02:28:57.593784Z", + "iopub.status.idle": "2023-08-21T02:28:57.596074Z", + "shell.execute_reply": "2023-08-21T02:28:57.595807Z" } }, "outputs": [ @@ -2117,7 +2117,7 @@ }, { "cell_type": "markdown", - "id": "8bc030e2", + "id": "47c8a520", "metadata": {}, "source": [ "The `QDA()` classifier will estimate one covariance per class. Here is the\n", @@ -2127,13 +2127,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "074c9d46", + "id": "2a3bb41e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.158287Z", - "iopub.status.busy": "2023-08-07T00:18:34.158209Z", - "iopub.status.idle": "2023-08-07T00:18:34.160379Z", - "shell.execute_reply": "2023-08-07T00:18:34.160138Z" + "iopub.execute_input": "2023-08-21T02:28:57.597540Z", + "iopub.status.busy": "2023-08-21T02:28:57.597455Z", + "iopub.status.idle": "2023-08-21T02:28:57.599658Z", + "shell.execute_reply": "2023-08-21T02:28:57.599399Z" }, "lines_to_next_cell": 0 }, @@ -2156,7 +2156,7 @@ }, { "cell_type": "markdown", - "id": "fa5963f6", + "id": "01a6ad38", "metadata": {}, "source": [ "The output contains the group means. But it does not contain the\n", @@ -2169,13 +2169,13 @@ { "cell_type": "code", "execution_count": 36, - "id": "3c95aae1", + "id": "1c64310b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.161750Z", - "iopub.status.busy": "2023-08-07T00:18:34.161677Z", - "iopub.status.idle": "2023-08-07T00:18:34.166050Z", - "shell.execute_reply": "2023-08-07T00:18:34.165798Z" + "iopub.execute_input": "2023-08-21T02:28:57.601097Z", + "iopub.status.busy": "2023-08-21T02:28:57.601019Z", + "iopub.status.idle": "2023-08-21T02:28:57.605729Z", + "shell.execute_reply": "2023-08-21T02:28:57.605444Z" }, "lines_to_next_cell": 0 }, @@ -2244,7 +2244,7 @@ }, { "cell_type": "markdown", - "id": "0a72c1b8", + "id": "7b797db4", "metadata": {}, "source": [ "Interestingly, the QDA predictions are accurate almost 60% of the\n", @@ -2254,13 +2254,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "935b2079", + "id": "0c05c5a8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.167439Z", - "iopub.status.busy": "2023-08-07T00:18:34.167360Z", - "iopub.status.idle": "2023-08-07T00:18:34.169654Z", - "shell.execute_reply": "2023-08-07T00:18:34.169417Z" + "iopub.execute_input": "2023-08-21T02:28:57.607207Z", + "iopub.status.busy": "2023-08-21T02:28:57.607093Z", + "iopub.status.idle": "2023-08-21T02:28:57.609626Z", + "shell.execute_reply": "2023-08-21T02:28:57.609348Z" }, "lines_to_next_cell": 2 }, @@ -2282,7 +2282,7 @@ }, { "cell_type": "markdown", - "id": "cbf4d7d1", + "id": "bb1a8707", "metadata": {}, "source": [ "This level of accuracy is quite impressive for stock market data, which is\n", @@ -2296,7 +2296,7 @@ }, { "cell_type": "markdown", - "id": "b89dfb88", + "id": "1bb713b7", "metadata": {}, "source": [ "## Naive Bayes\n", @@ -2310,13 +2310,13 @@ { "cell_type": "code", "execution_count": 38, - "id": "628b0355", + "id": "47d85305", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.171111Z", - "iopub.status.busy": "2023-08-07T00:18:34.171037Z", - "iopub.status.idle": "2023-08-07T00:18:34.174682Z", - "shell.execute_reply": "2023-08-07T00:18:34.174410Z" + "iopub.execute_input": "2023-08-21T02:28:57.611305Z", + "iopub.status.busy": "2023-08-21T02:28:57.611177Z", + "iopub.status.idle": "2023-08-21T02:28:57.615527Z", + "shell.execute_reply": "2023-08-21T02:28:57.615221Z" }, "lines_to_next_cell": 2 }, @@ -2342,7 +2342,7 @@ }, { "cell_type": "markdown", - "id": "66916c68", + "id": "363a04ab", "metadata": {}, "source": [ "The classes are stored as `classes_`." @@ -2351,13 +2351,13 @@ { "cell_type": "code", "execution_count": 39, - "id": "8a5fc302", + "id": "c553aadf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.176064Z", - "iopub.status.busy": "2023-08-07T00:18:34.175981Z", - "iopub.status.idle": "2023-08-07T00:18:34.178020Z", - "shell.execute_reply": "2023-08-07T00:18:34.177775Z" + "iopub.execute_input": "2023-08-21T02:28:57.617346Z", + "iopub.status.busy": "2023-08-21T02:28:57.617202Z", + "iopub.status.idle": "2023-08-21T02:28:57.619612Z", + "shell.execute_reply": "2023-08-21T02:28:57.619301Z" }, "lines_to_next_cell": 2 }, @@ -2379,7 +2379,7 @@ }, { "cell_type": "markdown", - "id": "753c7d37", + "id": "88ba284a", "metadata": {}, "source": [ "The class prior probabilities are stored in the `class_prior_` attribute." @@ -2388,13 +2388,13 @@ { "cell_type": "code", "execution_count": 40, - "id": "6b1b5cc6", + "id": "4604bd3d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.179469Z", - "iopub.status.busy": "2023-08-07T00:18:34.179381Z", - "iopub.status.idle": "2023-08-07T00:18:34.181492Z", - "shell.execute_reply": "2023-08-07T00:18:34.181231Z" + "iopub.execute_input": "2023-08-21T02:28:57.621294Z", + "iopub.status.busy": "2023-08-21T02:28:57.621168Z", + "iopub.status.idle": "2023-08-21T02:28:57.623355Z", + "shell.execute_reply": "2023-08-21T02:28:57.623086Z" }, "lines_to_next_cell": 2 }, @@ -2416,7 +2416,7 @@ }, { "cell_type": "markdown", - "id": "848e5d7c", + "id": "29471619", "metadata": {}, "source": [ "The parameters of the features can be found in the `theta_` and `var_` attributes. The number of rows\n", @@ -2427,13 +2427,13 @@ { "cell_type": "code", "execution_count": 41, - "id": "1a4b7cb0", + "id": "5ac2cabe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.182840Z", - "iopub.status.busy": "2023-08-07T00:18:34.182768Z", - "iopub.status.idle": "2023-08-07T00:18:34.184754Z", - "shell.execute_reply": "2023-08-07T00:18:34.184502Z" + "iopub.execute_input": "2023-08-21T02:28:57.624902Z", + "iopub.status.busy": "2023-08-21T02:28:57.624775Z", + "iopub.status.idle": "2023-08-21T02:28:57.627201Z", + "shell.execute_reply": "2023-08-21T02:28:57.626911Z" } }, "outputs": [ @@ -2455,7 +2455,7 @@ }, { "cell_type": "markdown", - "id": "60efd24c", + "id": "83986c67", "metadata": {}, "source": [ "Its variance is 1.503." @@ -2464,13 +2464,13 @@ { "cell_type": "code", "execution_count": 42, - "id": "f59d7cb4", + "id": "f8623945", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.186100Z", - "iopub.status.busy": "2023-08-07T00:18:34.186027Z", - "iopub.status.idle": "2023-08-07T00:18:34.188103Z", - "shell.execute_reply": "2023-08-07T00:18:34.187846Z" + "iopub.execute_input": "2023-08-21T02:28:57.628781Z", + "iopub.status.busy": "2023-08-21T02:28:57.628673Z", + "iopub.status.idle": "2023-08-21T02:28:57.631185Z", + "shell.execute_reply": "2023-08-21T02:28:57.630909Z" }, "lines_to_next_cell": 0 }, @@ -2493,7 +2493,7 @@ }, { "cell_type": "markdown", - "id": "4518d5db", + "id": "735e373e", "metadata": {}, "source": [ "How do we know the names of these attributes? We use `NB?` (or `?NB`).\n", @@ -2504,13 +2504,13 @@ { "cell_type": "code", "execution_count": 43, - "id": "aa50d0b8", + "id": "0790f26e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.189446Z", - "iopub.status.busy": "2023-08-07T00:18:34.189365Z", - "iopub.status.idle": "2023-08-07T00:18:34.192272Z", - "shell.execute_reply": "2023-08-07T00:18:34.192020Z" + "iopub.execute_input": "2023-08-21T02:28:57.632749Z", + "iopub.status.busy": "2023-08-21T02:28:57.632622Z", + "iopub.status.idle": "2023-08-21T02:28:57.635950Z", + "shell.execute_reply": "2023-08-21T02:28:57.635645Z" }, "lines_to_next_cell": 2 }, @@ -2534,7 +2534,7 @@ }, { "cell_type": "markdown", - "id": "3ee8e214", + "id": "d6c04b6f", "metadata": {}, "source": [ "Similarly for the variance:" @@ -2543,13 +2543,13 @@ { "cell_type": "code", "execution_count": 44, - "id": "3a2b0653", + "id": "4a8cf0ce", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.193606Z", - "iopub.status.busy": "2023-08-07T00:18:34.193530Z", - "iopub.status.idle": "2023-08-07T00:18:34.196330Z", - "shell.execute_reply": "2023-08-07T00:18:34.196080Z" + "iopub.execute_input": "2023-08-21T02:28:57.637431Z", + "iopub.status.busy": "2023-08-21T02:28:57.637345Z", + "iopub.status.idle": "2023-08-21T02:28:57.640679Z", + "shell.execute_reply": "2023-08-21T02:28:57.640387Z" }, "lines_to_next_cell": 0 }, @@ -2573,7 +2573,7 @@ }, { "cell_type": "markdown", - "id": "5cb3b59d", + "id": "ada40be5", "metadata": {}, "source": [ "Since `NB()` is a classifier in the `sklearn` library, making predictions\n", @@ -2583,13 +2583,13 @@ { "cell_type": "code", "execution_count": 45, - "id": "938f09ea", + "id": "94e7ff1a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.197723Z", - "iopub.status.busy": "2023-08-07T00:18:34.197649Z", - "iopub.status.idle": "2023-08-07T00:18:34.201814Z", - "shell.execute_reply": "2023-08-07T00:18:34.201560Z" + "iopub.execute_input": "2023-08-21T02:28:57.642306Z", + "iopub.status.busy": "2023-08-21T02:28:57.642190Z", + "iopub.status.idle": "2023-08-21T02:28:57.646804Z", + "shell.execute_reply": "2023-08-21T02:28:57.646534Z" } }, "outputs": [ @@ -2657,7 +2657,7 @@ }, { "cell_type": "markdown", - "id": "a0ce38d6", + "id": "83ee1f52", "metadata": {}, "source": [ "Naive Bayes performs well on these data, with accurate predictions over 59% of the time. This is slightly worse than QDA, but much better than LDA.\n", @@ -2668,13 +2668,13 @@ { "cell_type": "code", "execution_count": 46, - "id": "42f0e4a2", + "id": "137e23aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.203256Z", - "iopub.status.busy": "2023-08-07T00:18:34.203171Z", - "iopub.status.idle": "2023-08-07T00:18:34.206007Z", - "shell.execute_reply": "2023-08-07T00:18:34.205785Z" + "iopub.execute_input": "2023-08-21T02:28:57.648357Z", + "iopub.status.busy": "2023-08-21T02:28:57.648246Z", + "iopub.status.idle": "2023-08-21T02:28:57.651178Z", + "shell.execute_reply": "2023-08-21T02:28:57.650935Z" }, "lines_to_next_cell": 2 }, @@ -2700,7 +2700,7 @@ }, { "cell_type": "markdown", - "id": "ede194da", + "id": "52c4905b", "metadata": {}, "source": [ "## K-Nearest Neighbors\n", @@ -2718,13 +2718,13 @@ { "cell_type": "code", "execution_count": 47, - "id": "8b833ee4", + "id": "142c5217", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.207393Z", - "iopub.status.busy": "2023-08-07T00:18:34.207316Z", - "iopub.status.idle": "2023-08-07T00:18:34.217410Z", - "shell.execute_reply": "2023-08-07T00:18:34.217153Z" + "iopub.execute_input": "2023-08-21T02:28:57.652781Z", + "iopub.status.busy": "2023-08-21T02:28:57.652676Z", + "iopub.status.idle": "2023-08-21T02:28:57.665013Z", + "shell.execute_reply": "2023-08-21T02:28:57.664742Z" } }, "outputs": [ @@ -2795,7 +2795,7 @@ }, { "cell_type": "markdown", - "id": "5fb57a8d", + "id": "7122ab7d", "metadata": {}, "source": [ "The results using $K=1$ are not very good, since only $50%$ of the\n", @@ -2806,13 +2806,13 @@ { "cell_type": "code", "execution_count": 48, - "id": "45410b7f", + "id": "f5a272ee", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.218786Z", - "iopub.status.busy": "2023-08-07T00:18:34.218710Z", - "iopub.status.idle": "2023-08-07T00:18:34.221046Z", - "shell.execute_reply": "2023-08-07T00:18:34.220711Z" + "iopub.execute_input": "2023-08-21T02:28:57.666910Z", + "iopub.status.busy": "2023-08-21T02:28:57.666782Z", + "iopub.status.idle": "2023-08-21T02:28:57.669498Z", + "shell.execute_reply": "2023-08-21T02:28:57.669179Z" } }, "outputs": [ @@ -2833,7 +2833,7 @@ }, { "cell_type": "markdown", - "id": "500e00a1", + "id": "978e8677", "metadata": {}, "source": [ "We repeat the\n", @@ -2843,13 +2843,13 @@ { "cell_type": "code", "execution_count": 49, - "id": "ae6e0156", + "id": "95e206a8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.223557Z", - "iopub.status.busy": "2023-08-07T00:18:34.223449Z", - "iopub.status.idle": "2023-08-07T00:18:34.231477Z", - "shell.execute_reply": "2023-08-07T00:18:34.231224Z" + "iopub.execute_input": "2023-08-21T02:28:57.671045Z", + "iopub.status.busy": "2023-08-21T02:28:57.670939Z", + "iopub.status.idle": "2023-08-21T02:28:57.679000Z", + "shell.execute_reply": "2023-08-21T02:28:57.678715Z" }, "lines_to_next_cell": 0 }, @@ -2873,7 +2873,7 @@ }, { "cell_type": "markdown", - "id": "11a369af", + "id": "181bed9c", "metadata": {}, "source": [ "The results have improved slightly. But increasing *K* further\n", @@ -2884,7 +2884,7 @@ }, { "cell_type": "markdown", - "id": "ab3b1e64", + "id": "07a9cc30", "metadata": {}, "source": [ "KNN does not perform well on the `Smarket` data, but it often does provide impressive results. As an example we will apply the KNN approach to the `Caravan` data set, which is part of the `ISLP` library. This data set includes 85\n", @@ -2898,13 +2898,13 @@ { "cell_type": "code", "execution_count": 50, - "id": "9338564d", + "id": "422563b7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.233037Z", - "iopub.status.busy": "2023-08-07T00:18:34.232954Z", - "iopub.status.idle": "2023-08-07T00:18:34.250597Z", - "shell.execute_reply": "2023-08-07T00:18:34.250317Z" + "iopub.execute_input": "2023-08-21T02:28:57.680643Z", + "iopub.status.busy": "2023-08-21T02:28:57.680560Z", + "iopub.status.idle": "2023-08-21T02:28:57.698929Z", + "shell.execute_reply": "2023-08-21T02:28:57.698652Z" }, "lines_to_next_cell": 2 }, @@ -2930,7 +2930,7 @@ }, { "cell_type": "markdown", - "id": "ba5e14a9", + "id": "1d928895", "metadata": {}, "source": [ "The method `value_counts()` takes a `pd.Series` or `pd.DataFrame` and returns\n", @@ -2942,13 +2942,13 @@ { "cell_type": "code", "execution_count": 51, - "id": "a94f2ea7", + "id": "583c860c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.252089Z", - "iopub.status.busy": "2023-08-07T00:18:34.252008Z", - "iopub.status.idle": "2023-08-07T00:18:34.254154Z", - "shell.execute_reply": "2023-08-07T00:18:34.253908Z" + "iopub.execute_input": "2023-08-21T02:28:57.700534Z", + "iopub.status.busy": "2023-08-21T02:28:57.700406Z", + "iopub.status.idle": "2023-08-21T02:28:57.702790Z", + "shell.execute_reply": "2023-08-21T02:28:57.702539Z" }, "lines_to_next_cell": 2 }, @@ -2970,7 +2970,7 @@ }, { "cell_type": "markdown", - "id": "a753d948", + "id": "c4f745d5", "metadata": {}, "source": [ "Our features will include all columns except `Purchase`." @@ -2979,13 +2979,13 @@ { "cell_type": "code", "execution_count": 52, - "id": "6b6438b0", + "id": "19ee3bf2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.255519Z", - "iopub.status.busy": "2023-08-07T00:18:34.255441Z", - "iopub.status.idle": "2023-08-07T00:18:34.258008Z", - "shell.execute_reply": "2023-08-07T00:18:34.257754Z" + "iopub.execute_input": "2023-08-21T02:28:57.704427Z", + "iopub.status.busy": "2023-08-21T02:28:57.704308Z", + "iopub.status.idle": "2023-08-21T02:28:57.707107Z", + "shell.execute_reply": "2023-08-21T02:28:57.706716Z" } }, "outputs": [], @@ -2995,7 +2995,7 @@ }, { "cell_type": "markdown", - "id": "52a62ab9", + "id": "3b9a30b4", "metadata": {}, "source": [ "Because the KNN classifier predicts the class of a given test\n", @@ -3028,13 +3028,13 @@ { "cell_type": "code", "execution_count": 53, - "id": "158bfb3f", + "id": "fdc0e5f1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.259410Z", - "iopub.status.busy": "2023-08-07T00:18:34.259332Z", - "iopub.status.idle": "2023-08-07T00:18:34.261018Z", - "shell.execute_reply": "2023-08-07T00:18:34.260785Z" + "iopub.execute_input": "2023-08-21T02:28:57.708836Z", + "iopub.status.busy": "2023-08-21T02:28:57.708707Z", + "iopub.status.idle": "2023-08-21T02:28:57.710507Z", + "shell.execute_reply": "2023-08-21T02:28:57.710220Z" }, "lines_to_next_cell": 0 }, @@ -3047,7 +3047,7 @@ }, { "cell_type": "markdown", - "id": "45baf26c", + "id": "7613cdcd", "metadata": {}, "source": [ "The argument `with_mean` indicates whether or not\n", @@ -3067,13 +3067,13 @@ { "cell_type": "code", "execution_count": 54, - "id": "d11b04c4", + "id": "2bb9d48b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.262381Z", - "iopub.status.busy": "2023-08-07T00:18:34.262309Z", - "iopub.status.idle": "2023-08-07T00:18:34.268511Z", - "shell.execute_reply": "2023-08-07T00:18:34.268240Z" + "iopub.execute_input": "2023-08-21T02:28:57.712025Z", + "iopub.status.busy": "2023-08-21T02:28:57.711919Z", + "iopub.status.idle": "2023-08-21T02:28:57.717961Z", + "shell.execute_reply": "2023-08-21T02:28:57.717623Z" }, "lines_to_next_cell": 0 }, @@ -3085,7 +3085,7 @@ }, { "cell_type": "markdown", - "id": "f388fe5b", + "id": "fb65f78c", "metadata": {}, "source": [ "Now every column of `feature_std` below has a standard deviation of\n", @@ -3095,13 +3095,13 @@ { "cell_type": "code", "execution_count": 55, - "id": "f0c65b34", + "id": "649b57b3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.270915Z", - "iopub.status.busy": "2023-08-07T00:18:34.270815Z", - "iopub.status.idle": "2023-08-07T00:18:34.280162Z", - "shell.execute_reply": "2023-08-07T00:18:34.279871Z" + "iopub.execute_input": "2023-08-21T02:28:57.719733Z", + "iopub.status.busy": "2023-08-21T02:28:57.719643Z", + "iopub.status.idle": "2023-08-21T02:28:57.725788Z", + "shell.execute_reply": "2023-08-21T02:28:57.725511Z" } }, "outputs": [ @@ -3136,10 +3136,10 @@ }, { "cell_type": "markdown", - "id": "9668a83b", + "id": "758fa566", "metadata": {}, "source": [ - "Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 103.\n", + "Notice that the standard deviations are not quite $1$ here; this is again due to some procedures using the $1/n$ convention for variances (in this case `scaler()`), while others use $1/(n-1)$ (the `std()` method). See the footnote on page 200.\n", "In this case it does not matter, as long as the variables are all on the same scale.\n", "\n", "Using the function `train_test_split()` we now split the observations into a test set,\n", @@ -3151,13 +3151,13 @@ { "cell_type": "code", "execution_count": 56, - "id": "3861cc4f", + "id": "d0aafd5e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.281711Z", - "iopub.status.busy": "2023-08-07T00:18:34.281622Z", - "iopub.status.idle": "2023-08-07T00:18:34.285831Z", - "shell.execute_reply": "2023-08-07T00:18:34.285544Z" + "iopub.execute_input": "2023-08-21T02:28:57.727208Z", + "iopub.status.busy": "2023-08-21T02:28:57.727114Z", + "iopub.status.idle": "2023-08-21T02:28:57.730769Z", + "shell.execute_reply": "2023-08-21T02:28:57.730507Z" }, "lines_to_next_cell": 0 }, @@ -3174,7 +3174,7 @@ }, { "cell_type": "markdown", - "id": "3cd24d0a", + "id": "03b585b9", "metadata": {}, "source": [ "`?train_test_split` reveals that the non-keyword arguments can be `lists`, `arrays`, `pandas dataframes` etc that all have the same length (`shape[0]`) and hence are *indexable*. In this case they are the dataframe `feature_std` and the response variable `Purchase`.\n", @@ -3186,13 +3186,13 @@ { "cell_type": "code", "execution_count": 57, - "id": "1b9d7b32", + "id": "ad02fb42", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.287335Z", - "iopub.status.busy": "2023-08-07T00:18:34.287257Z", - "iopub.status.idle": "2023-08-07T00:18:34.618181Z", - "shell.execute_reply": "2023-08-07T00:18:34.617751Z" + "iopub.execute_input": "2023-08-21T02:28:57.732302Z", + "iopub.status.busy": "2023-08-21T02:28:57.732202Z", + "iopub.status.idle": "2023-08-21T02:28:58.059169Z", + "shell.execute_reply": "2023-08-21T02:28:58.058739Z" }, "lines_to_next_cell": 2 }, @@ -3216,7 +3216,7 @@ }, { "cell_type": "markdown", - "id": "0464c302", + "id": "b9dd0550", "metadata": {}, "source": [ "The KNN error rate on the 1,000 test observations is about $11%$.\n", @@ -3239,13 +3239,13 @@ { "cell_type": "code", "execution_count": 58, - "id": "d683b2f9", + "id": "901f772e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.620146Z", - "iopub.status.busy": "2023-08-07T00:18:34.620027Z", - "iopub.status.idle": "2023-08-07T00:18:34.626198Z", - "shell.execute_reply": "2023-08-07T00:18:34.625901Z" + "iopub.execute_input": "2023-08-21T02:28:58.061293Z", + "iopub.status.busy": "2023-08-21T02:28:58.061165Z", + "iopub.status.idle": "2023-08-21T02:28:58.067519Z", + "shell.execute_reply": "2023-08-21T02:28:58.067152Z" } }, "outputs": [ @@ -3312,7 +3312,7 @@ }, { "cell_type": "markdown", - "id": "19ed36b1", + "id": "b6260417", "metadata": {}, "source": [ "It turns out that KNN with $K=1$ does far better than random guessing\n", @@ -3324,13 +3324,13 @@ { "cell_type": "code", "execution_count": 59, - "id": "3498856a", + "id": "684f8941", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.627931Z", - "iopub.status.busy": "2023-08-07T00:18:34.627821Z", - "iopub.status.idle": "2023-08-07T00:18:34.630085Z", - "shell.execute_reply": "2023-08-07T00:18:34.629798Z" + "iopub.execute_input": "2023-08-21T02:28:58.069340Z", + "iopub.status.busy": "2023-08-21T02:28:58.069223Z", + "iopub.status.idle": "2023-08-21T02:28:58.071737Z", + "shell.execute_reply": "2023-08-21T02:28:58.071247Z" }, "lines_to_next_cell": 2 }, @@ -3352,7 +3352,7 @@ }, { "cell_type": "markdown", - "id": "3ec215b9", + "id": "19dcfad2", "metadata": {}, "source": [ "### Tuning Parameters\n", @@ -3368,13 +3368,13 @@ { "cell_type": "code", "execution_count": 60, - "id": "9ed36c50", + "id": "4d984cf0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.631783Z", - "iopub.status.busy": "2023-08-07T00:18:34.631676Z", - "iopub.status.idle": "2023-08-07T00:18:34.704358Z", - "shell.execute_reply": "2023-08-07T00:18:34.703933Z" + "iopub.execute_input": "2023-08-21T02:28:58.073571Z", + "iopub.status.busy": "2023-08-21T02:28:58.073457Z", + "iopub.status.idle": "2023-08-21T02:28:58.146516Z", + "shell.execute_reply": "2023-08-21T02:28:58.146020Z" }, "lines_to_next_cell": 0 }, @@ -3409,7 +3409,7 @@ }, { "cell_type": "markdown", - "id": "0718d8f0", + "id": "04c42362", "metadata": {}, "source": [ "We see some variability --- the numbers for `K=4` are very different from the rest.\n", @@ -3435,13 +3435,13 @@ { "cell_type": "code", "execution_count": 61, - "id": "8605a327", + "id": "d24f4e50", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:34.706353Z", - "iopub.status.busy": "2023-08-07T00:18:34.706235Z", - "iopub.status.idle": "2023-08-07T00:18:35.327825Z", - "shell.execute_reply": "2023-08-07T00:18:35.327057Z" + "iopub.execute_input": "2023-08-21T02:28:58.148624Z", + "iopub.status.busy": "2023-08-21T02:28:58.148360Z", + "iopub.status.idle": "2023-08-21T02:28:58.773984Z", + "shell.execute_reply": "2023-08-21T02:28:58.773325Z" } }, "outputs": [ @@ -3512,7 +3512,7 @@ }, { "cell_type": "markdown", - "id": "daf1655f", + "id": "f2d91f33", "metadata": {}, "source": [ "We used the argument `solver='liblinear'` above to\n", @@ -3532,13 +3532,13 @@ { "cell_type": "code", "execution_count": 62, - "id": "e686f675", + "id": "25152580", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.331998Z", - "iopub.status.busy": "2023-08-07T00:18:35.331623Z", - "iopub.status.idle": "2023-08-07T00:18:35.345291Z", - "shell.execute_reply": "2023-08-07T00:18:35.344659Z" + "iopub.execute_input": "2023-08-21T02:28:58.778052Z", + "iopub.status.busy": "2023-08-21T02:28:58.777754Z", + "iopub.status.idle": "2023-08-21T02:28:58.791505Z", + "shell.execute_reply": "2023-08-21T02:28:58.790362Z" } }, "outputs": [ @@ -3607,13 +3607,13 @@ { "cell_type": "code", "execution_count": 63, - "id": "d92cfcd4", + "id": "b8ea6e08", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.349220Z", - "iopub.status.busy": "2023-08-07T00:18:35.348866Z", - "iopub.status.idle": "2023-08-07T00:18:35.355490Z", - "shell.execute_reply": "2023-08-07T00:18:35.354787Z" + "iopub.execute_input": "2023-08-21T02:28:58.798441Z", + "iopub.status.busy": "2023-08-21T02:28:58.798187Z", + "iopub.status.idle": "2023-08-21T02:28:58.805015Z", + "shell.execute_reply": "2023-08-21T02:28:58.804266Z" }, "lines_to_next_cell": 0 }, @@ -3635,7 +3635,7 @@ }, { "cell_type": "markdown", - "id": "8a0c1140", + "id": "bb96ed25", "metadata": {}, "source": [ "## Linear and Poisson Regression on the Bikeshare Data\n", @@ -3647,13 +3647,13 @@ { "cell_type": "code", "execution_count": 64, - "id": "6f93060c", + "id": "def80d79", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.359503Z", - "iopub.status.busy": "2023-08-07T00:18:35.359134Z", - "iopub.status.idle": "2023-08-07T00:18:35.374649Z", - "shell.execute_reply": "2023-08-07T00:18:35.373898Z" + "iopub.execute_input": "2023-08-21T02:28:58.808423Z", + "iopub.status.busy": "2023-08-21T02:28:58.807929Z", + "iopub.status.idle": "2023-08-21T02:28:58.826147Z", + "shell.execute_reply": "2023-08-21T02:28:58.825308Z" }, "lines_to_next_cell": 0 }, @@ -3664,7 +3664,7 @@ }, { "cell_type": "markdown", - "id": "af82d61e", + "id": "146abe69", "metadata": {}, "source": [ "Let's have a peek at the dimensions and names of the variables in this dataframe." @@ -3673,13 +3673,13 @@ { "cell_type": "code", "execution_count": 65, - "id": "725ce173", + "id": "f899d5ab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.378452Z", - "iopub.status.busy": "2023-08-07T00:18:35.378048Z", - "iopub.status.idle": "2023-08-07T00:18:35.382634Z", - "shell.execute_reply": "2023-08-07T00:18:35.382022Z" + "iopub.execute_input": "2023-08-21T02:28:58.838005Z", + "iopub.status.busy": "2023-08-21T02:28:58.837542Z", + "iopub.status.idle": "2023-08-21T02:28:58.842697Z", + "shell.execute_reply": "2023-08-21T02:28:58.841989Z" } }, "outputs": [ @@ -3704,7 +3704,7 @@ }, { "cell_type": "markdown", - "id": "9b133dc3", + "id": "b07be452", "metadata": {}, "source": [ "### Linear Regression\n", @@ -3715,13 +3715,13 @@ { "cell_type": "code", "execution_count": 66, - "id": "ee3e5e3b", + "id": "76f4cea5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.386155Z", - "iopub.status.busy": "2023-08-07T00:18:35.385658Z", - "iopub.status.idle": "2023-08-07T00:18:35.457335Z", - "shell.execute_reply": "2023-08-07T00:18:35.456140Z" + "iopub.execute_input": "2023-08-21T02:28:58.846154Z", + "iopub.status.busy": "2023-08-21T02:28:58.845860Z", + "iopub.status.idle": "2023-08-21T02:28:58.936368Z", + "shell.execute_reply": "2023-08-21T02:28:58.931496Z" } }, "outputs": [ @@ -3755,86 +3755,86 @@ " \n", " \n", " intercept\n", - " -27.2068\n", - " 6.715\n", - " -4.052\n", + " -68.6317\n", + " 5.307\n", + " -12.932\n", " 0.000\n", " \n", " \n", - " mnth[Aug]\n", - " 11.8181\n", - " 4.698\n", - " 2.515\n", - " 0.012\n", - " \n", - " \n", - " mnth[Dec]\n", - " 5.0328\n", - " 4.280\n", - " 1.176\n", - " 0.240\n", - " \n", - " \n", " mnth[Feb]\n", - " -34.5797\n", - " 4.575\n", - " -7.558\n", - " 0.000\n", - " \n", - " \n", - " mnth[Jan]\n", - " -41.4249\n", - " 4.972\n", - " -8.331\n", - " 0.000\n", - " \n", - " \n", - " mnth[July]\n", - " 3.8996\n", - " 5.003\n", - " 0.779\n", - " 0.436\n", - " \n", - " \n", - " mnth[June]\n", - " 26.3938\n", - " 4.642\n", - " 5.686\n", - " 0.000\n", + " 6.8452\n", + " 4.287\n", + " 1.597\n", + " 0.110\n", " \n", " \n", " mnth[March]\n", - " -24.8735\n", - " 4.277\n", - " -5.815\n", + " 16.5514\n", + " 4.301\n", + " 3.848\n", + " 0.000\n", + " \n", + " \n", + " mnth[April]\n", + " 41.4249\n", + " 4.972\n", + " 8.331\n", " 0.000\n", " \n", " \n", " mnth[May]\n", - " 31.1322\n", - " 4.150\n", - " 7.501\n", + " 72.5571\n", + " 5.641\n", + " 12.862\n", " 0.000\n", " \n", " \n", - " mnth[Nov]\n", - " 18.8851\n", - " 4.099\n", - " 4.607\n", + " mnth[June]\n", + " 67.8187\n", + " 6.544\n", + " 10.364\n", " 0.000\n", " \n", " \n", - " mnth[Oct]\n", - " 34.4093\n", - " 4.006\n", - " 8.589\n", + " mnth[July]\n", + " 45.3245\n", + " 7.081\n", + " 6.401\n", + " 0.000\n", + " \n", + " \n", + " mnth[Aug]\n", + " 53.2430\n", + " 6.640\n", + " 8.019\n", " 0.000\n", " \n", " \n", " mnth[Sept]\n", - " 25.2534\n", - " 4.293\n", - " 5.883\n", + " 66.6783\n", + " 5.925\n", + " 11.254\n", + " 0.000\n", + " \n", + " \n", + " mnth[Oct]\n", + " 75.8343\n", + " 4.950\n", + " 15.319\n", + " 0.000\n", + " \n", + " \n", + " mnth[Nov]\n", + " 60.3100\n", + " 4.610\n", + " 13.083\n", + " 0.000\n", + " \n", + " \n", + " mnth[Dec]\n", + " 46.4577\n", + " 4.271\n", + " 10.878\n", " 0.000\n", " \n", " \n", @@ -4039,18 +4039,18 @@ ], "text/plain": [ " coef std err t P>|t|\n", - "intercept -27.2068 6.715 -4.052 0.000\n", - "mnth[Aug] 11.8181 4.698 2.515 0.012\n", - "mnth[Dec] 5.0328 4.280 1.176 0.240\n", - "mnth[Feb] -34.5797 4.575 -7.558 0.000\n", - "mnth[Jan] -41.4249 4.972 -8.331 0.000\n", - "mnth[July] 3.8996 5.003 0.779 0.436\n", - "mnth[June] 26.3938 4.642 5.686 0.000\n", - "mnth[March] -24.8735 4.277 -5.815 0.000\n", - "mnth[May] 31.1322 4.150 7.501 0.000\n", - "mnth[Nov] 18.8851 4.099 4.607 0.000\n", - "mnth[Oct] 34.4093 4.006 8.589 0.000\n", - "mnth[Sept] 25.2534 4.293 5.883 0.000\n", + "intercept -68.6317 5.307 -12.932 0.000\n", + "mnth[Feb] 6.8452 4.287 1.597 0.110\n", + "mnth[March] 16.5514 4.301 3.848 0.000\n", + "mnth[April] 41.4249 4.972 8.331 0.000\n", + "mnth[May] 72.5571 5.641 12.862 0.000\n", + "mnth[June] 67.8187 6.544 10.364 0.000\n", + "mnth[July] 45.3245 7.081 6.401 0.000\n", + "mnth[Aug] 53.2430 6.640 8.019 0.000\n", + "mnth[Sept] 66.6783 5.925 11.254 0.000\n", + "mnth[Oct] 75.8343 4.950 15.319 0.000\n", + "mnth[Nov] 60.3100 4.610 13.083 0.000\n", + "mnth[Dec] 46.4577 4.271 10.878 0.000\n", "hr[1] -14.5793 5.699 -2.558 0.011\n", "hr[2] -21.5791 5.733 -3.764 0.000\n", "hr[3] -31.1408 5.778 -5.389 0.000\n", @@ -4099,7 +4099,7 @@ }, { "cell_type": "markdown", - "id": "36f3063f", + "id": "46d55b3d", "metadata": {}, "source": [ "There are 24 levels in `hr` and 40 rows in all.\n", @@ -4119,13 +4119,13 @@ { "cell_type": "code", "execution_count": 67, - "id": "d6a047f5", + "id": "5778ada8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.461563Z", - "iopub.status.busy": "2023-08-07T00:18:35.460853Z", - "iopub.status.idle": "2023-08-07T00:18:35.464618Z", - "shell.execute_reply": "2023-08-07T00:18:35.463961Z" + "iopub.execute_input": "2023-08-21T02:28:58.941746Z", + "iopub.status.busy": "2023-08-21T02:28:58.941366Z", + "iopub.status.idle": "2023-08-21T02:28:58.948890Z", + "shell.execute_reply": "2023-08-21T02:28:58.947630Z" }, "lines_to_next_cell": 0 }, @@ -4137,7 +4137,7 @@ }, { "cell_type": "markdown", - "id": "6ed435ca", + "id": "06849d92", "metadata": {}, "source": [ "Refitting again:" @@ -4146,13 +4146,13 @@ { "cell_type": "code", "execution_count": 68, - "id": "d7f6edaf", + "id": "c6da14b9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.467867Z", - "iopub.status.busy": "2023-08-07T00:18:35.467482Z", - "iopub.status.idle": "2023-08-07T00:18:35.536860Z", - "shell.execute_reply": "2023-08-07T00:18:35.535938Z" + "iopub.execute_input": "2023-08-21T02:28:58.953721Z", + "iopub.status.busy": "2023-08-21T02:28:58.953330Z", + "iopub.status.idle": "2023-08-21T02:28:59.054283Z", + "shell.execute_reply": "2023-08-21T02:28:59.053564Z" } }, "outputs": [ @@ -4530,7 +4530,7 @@ }, { "cell_type": "markdown", - "id": "edea9530", + "id": "62d884bd", "metadata": {}, "source": [ "What is the difference between the two codings? In `M2_lm`, a\n", @@ -4556,20 +4556,20 @@ { "cell_type": "code", "execution_count": 69, - "id": "412ce964", + "id": "461d57c5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.541020Z", - "iopub.status.busy": "2023-08-07T00:18:35.540717Z", - "iopub.status.idle": "2023-08-07T00:18:35.554132Z", - "shell.execute_reply": "2023-08-07T00:18:35.552973Z" + "iopub.execute_input": "2023-08-21T02:28:59.063565Z", + "iopub.status.busy": "2023-08-21T02:28:59.063233Z", + "iopub.status.idle": "2023-08-21T02:28:59.071787Z", + "shell.execute_reply": "2023-08-21T02:28:59.070620Z" } }, "outputs": [ { "data": { "text/plain": [ - "4.186226544780569e-19" + "1.0353919305327476e-18" ] }, "execution_count": 69, @@ -4583,7 +4583,7 @@ }, { "cell_type": "markdown", - "id": "69539d1c", + "id": "a5a50827", "metadata": {}, "source": [ "The sum of squared differences is zero. We can also see this using the\n", @@ -4593,13 +4593,13 @@ { "cell_type": "code", "execution_count": 70, - "id": "5afd0d5d", + "id": "05d33247", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.559035Z", - "iopub.status.busy": "2023-08-07T00:18:35.558329Z", - "iopub.status.idle": "2023-08-07T00:18:35.566101Z", - "shell.execute_reply": "2023-08-07T00:18:35.565469Z" + "iopub.execute_input": "2023-08-21T02:28:59.077767Z", + "iopub.status.busy": "2023-08-21T02:28:59.077090Z", + "iopub.status.idle": "2023-08-21T02:28:59.085265Z", + "shell.execute_reply": "2023-08-21T02:28:59.084291Z" }, "lines_to_next_cell": 2 }, @@ -4621,7 +4621,7 @@ }, { "cell_type": "markdown", - "id": "fdc83f2e", + "id": "41256e16", "metadata": {}, "source": [ "To reproduce the left-hand side of Figure 4.13\n", @@ -4636,13 +4636,13 @@ { "cell_type": "code", "execution_count": 71, - "id": "4629f2c9", + "id": "bee42b38", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.570679Z", - "iopub.status.busy": "2023-08-07T00:18:35.570231Z", - "iopub.status.idle": "2023-08-07T00:18:35.579784Z", - "shell.execute_reply": "2023-08-07T00:18:35.579027Z" + "iopub.execute_input": "2023-08-21T02:28:59.089190Z", + "iopub.status.busy": "2023-08-21T02:28:59.088696Z", + "iopub.status.idle": "2023-08-21T02:28:59.099445Z", + "shell.execute_reply": "2023-08-21T02:28:59.098750Z" }, "lines_to_next_cell": 0 }, @@ -4676,7 +4676,7 @@ }, { "cell_type": "markdown", - "id": "6fdd5f23", + "id": "976ff3e0", "metadata": {}, "source": [ "Next, we append `Dec` as the negative of the sum of all other months." @@ -4685,13 +4685,13 @@ { "cell_type": "code", "execution_count": 72, - "id": "b3e55833", + "id": "4aa60857", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.583928Z", - "iopub.status.busy": "2023-08-07T00:18:35.583623Z", - "iopub.status.idle": "2023-08-07T00:18:35.592868Z", - "shell.execute_reply": "2023-08-07T00:18:35.592219Z" + "iopub.execute_input": "2023-08-21T02:28:59.103423Z", + "iopub.status.busy": "2023-08-21T02:28:59.103057Z", + "iopub.status.idle": "2023-08-21T02:28:59.112346Z", + "shell.execute_reply": "2023-08-21T02:28:59.111002Z" }, "lines_to_next_cell": 0 }, @@ -4732,7 +4732,7 @@ }, { "cell_type": "markdown", - "id": "4d1f1d30", + "id": "39be6060", "metadata": {}, "source": [ "Finally, to make the plot neater, we’ll just use the first letter of each month, which is the $6$th entry of each of\n", @@ -4742,13 +4742,13 @@ { "cell_type": "code", "execution_count": 73, - "id": "cdbebc03", + "id": "894d3e2c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.597005Z", - "iopub.status.busy": "2023-08-07T00:18:35.596605Z", - "iopub.status.idle": "2023-08-07T00:18:35.727830Z", - "shell.execute_reply": "2023-08-07T00:18:35.726982Z" + "iopub.execute_input": "2023-08-21T02:28:59.116495Z", + "iopub.status.busy": "2023-08-21T02:28:59.116193Z", + "iopub.status.idle": "2023-08-21T02:28:59.250351Z", + "shell.execute_reply": "2023-08-21T02:28:59.249608Z" } }, "outputs": [ @@ -4775,7 +4775,7 @@ }, { "cell_type": "markdown", - "id": "6f845be8", + "id": "fe1d213c", "metadata": {}, "source": [ "Reproducing the right-hand plot in Figure 4.13 follows a similar process." @@ -4784,13 +4784,13 @@ { "cell_type": "code", "execution_count": 74, - "id": "170fd6fd", + "id": "d636746e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.733543Z", - "iopub.status.busy": "2023-08-07T00:18:35.731394Z", - "iopub.status.idle": "2023-08-07T00:18:35.740654Z", - "shell.execute_reply": "2023-08-07T00:18:35.740001Z" + "iopub.execute_input": "2023-08-21T02:28:59.253708Z", + "iopub.status.busy": "2023-08-21T02:28:59.253326Z", + "iopub.status.idle": "2023-08-21T02:28:59.260644Z", + "shell.execute_reply": "2023-08-21T02:28:59.259501Z" } }, "outputs": [], @@ -4804,7 +4804,7 @@ }, { "cell_type": "markdown", - "id": "100190e3", + "id": "c181f106", "metadata": {}, "source": [ "We now make the hour plot." @@ -4813,13 +4813,13 @@ { "cell_type": "code", "execution_count": 75, - "id": "1e09e126", + "id": "ce6a1623", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.744368Z", - "iopub.status.busy": "2023-08-07T00:18:35.744031Z", - "iopub.status.idle": "2023-08-07T00:18:35.880750Z", - "shell.execute_reply": "2023-08-07T00:18:35.880425Z" + "iopub.execute_input": "2023-08-21T02:28:59.264364Z", + "iopub.status.busy": "2023-08-21T02:28:59.264112Z", + "iopub.status.idle": "2023-08-21T02:28:59.384102Z", + "shell.execute_reply": "2023-08-21T02:28:59.383714Z" } }, "outputs": [ @@ -4846,7 +4846,7 @@ }, { "cell_type": "markdown", - "id": "bf730e5d", + "id": "8cafb6b7", "metadata": {}, "source": [ "### Poisson Regression\n", @@ -4859,13 +4859,13 @@ { "cell_type": "code", "execution_count": 76, - "id": "a6667dcf", + "id": "9fb8b759", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.882429Z", - "iopub.status.busy": "2023-08-07T00:18:35.882313Z", - "iopub.status.idle": "2023-08-07T00:18:35.968054Z", - "shell.execute_reply": "2023-08-07T00:18:35.967220Z" + "iopub.execute_input": "2023-08-21T02:28:59.386009Z", + "iopub.status.busy": "2023-08-21T02:28:59.385867Z", + "iopub.status.idle": "2023-08-21T02:28:59.478822Z", + "shell.execute_reply": "2023-08-21T02:28:59.477841Z" } }, "outputs": [], @@ -4875,7 +4875,7 @@ }, { "cell_type": "markdown", - "id": "8309c1fc", + "id": "9cc0ea6d", "metadata": {}, "source": [ "We can plot the coefficients associated with `mnth` and `hr`, in order to reproduce Figure 4.15. We first complete these coefficients as before." @@ -4884,13 +4884,13 @@ { "cell_type": "code", "execution_count": 77, - "id": "c20de5d8", + "id": "ee272341", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:35.973009Z", - "iopub.status.busy": "2023-08-07T00:18:35.972664Z", - "iopub.status.idle": "2023-08-07T00:18:35.996642Z", - "shell.execute_reply": "2023-08-07T00:18:35.996051Z" + "iopub.execute_input": "2023-08-21T02:28:59.482988Z", + "iopub.status.busy": "2023-08-21T02:28:59.482517Z", + "iopub.status.idle": "2023-08-21T02:28:59.506926Z", + "shell.execute_reply": "2023-08-21T02:28:59.506267Z" }, "lines_to_next_cell": 0 }, @@ -4909,7 +4909,7 @@ }, { "cell_type": "markdown", - "id": "41e58943", + "id": "4a86df8c", "metadata": {}, "source": [ "The plotting is as before." @@ -4918,13 +4918,13 @@ { "cell_type": "code", "execution_count": 78, - "id": "4a1a8488", + "id": "1f5bde07", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:36.000164Z", - "iopub.status.busy": "2023-08-07T00:18:35.999622Z", - "iopub.status.idle": "2023-08-07T00:18:36.204622Z", - "shell.execute_reply": "2023-08-07T00:18:36.203590Z" + "iopub.execute_input": "2023-08-21T02:28:59.510834Z", + "iopub.status.busy": "2023-08-21T02:28:59.510470Z", + "iopub.status.idle": "2023-08-21T02:28:59.792175Z", + "shell.execute_reply": "2023-08-21T02:28:59.791845Z" }, "lines_to_next_cell": 0 }, @@ -4933,7 +4933,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_19145/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_80940/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n" ] }, @@ -4963,7 +4963,7 @@ }, { "cell_type": "markdown", - "id": "5e74d13e", + "id": "ff00b931", "metadata": {}, "source": [ "We compare the fitted values of the two models.\n", @@ -4975,13 +4975,13 @@ { "cell_type": "code", "execution_count": 79, - "id": "a3158334", + "id": "b0bd66a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:36.209262Z", - "iopub.status.busy": "2023-08-07T00:18:36.208838Z", - "iopub.status.idle": "2023-08-07T00:18:36.338262Z", - "shell.execute_reply": "2023-08-07T00:18:36.337899Z" + "iopub.execute_input": "2023-08-21T02:28:59.793823Z", + "iopub.status.busy": "2023-08-21T02:28:59.793732Z", + "iopub.status.idle": "2023-08-21T02:28:59.904438Z", + "shell.execute_reply": "2023-08-21T02:28:59.904151Z" } }, "outputs": [ @@ -5009,7 +5009,7 @@ }, { "cell_type": "markdown", - "id": "2b03ac4f", + "id": "a3b4b06e", "metadata": {}, "source": [ "The predictions from the Poisson regression model are correlated with\n", diff --git a/Ch05-resample-lab.Rmd b/Ch05-resample-lab.Rmd index 0b606f7..34e2d1d 100644 --- a/Ch05-resample-lab.Rmd +++ b/Ch05-resample-lab.Rmd @@ -518,13 +518,13 @@ slope. Interestingly, these are somewhat different from the estimates obtained using the bootstrap. Does this indicate a problem with the bootstrap? In fact, it suggests the opposite. Recall that the standard formulas given in - {Equation 3.8 on page 80} + {Equation 3.8 on page 82} rely on certain assumptions. For example, they depend on the unknown parameter $\sigma^2$, the noise variance. We then estimate $\sigma^2$ using the RSS. Now although the formula for the standard errors do not rely on the linear model being correct, the estimate for $\sigma^2$ does. We see - {in Figure 3.8 on page 106} that there is + {in Figure 3.8 on page 108} that there is a non-linear relationship in the data, and so the residuals from a linear fit will be inflated, and so will $\hat{\sigma}^2$. Secondly, the standard formulas assume (somewhat unrealistically) that the $x_i$ diff --git a/Ch05-resample-lab.ipynb b/Ch05-resample-lab.ipynb index 3a18390..85c1f65 100644 --- a/Ch05-resample-lab.ipynb +++ b/Ch05-resample-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3a3f2f85", + "id": "85ad9863", "metadata": {}, "source": [ "\n", @@ -12,7 +12,7 @@ }, { "cell_type": "markdown", - "id": "bb22af17", + "id": "ac8b08af", "metadata": {}, "source": [ "# Lab: Cross-Validation and the Bootstrap\n", @@ -26,13 +26,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "60fad148", + "id": "e7712cfe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:37.622425Z", - "iopub.status.busy": "2023-08-07T00:18:37.621828Z", - "iopub.status.idle": "2023-08-07T00:18:38.459128Z", - "shell.execute_reply": "2023-08-07T00:18:38.458689Z" + "iopub.execute_input": "2023-08-21T02:29:01.252458Z", + "iopub.status.busy": "2023-08-21T02:29:01.251970Z", + "iopub.status.idle": "2023-08-21T02:29:02.044045Z", + "shell.execute_reply": "2023-08-21T02:29:02.043730Z" }, "lines_to_next_cell": 2 }, @@ -49,7 +49,7 @@ }, { "cell_type": "markdown", - "id": "78fcfe7a", + "id": "784a2ba3", "metadata": {}, "source": [ "There are several new imports needed for this lab." @@ -58,13 +58,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "2478aeb4", + "id": "21c2ed4f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.461290Z", - "iopub.status.busy": "2023-08-07T00:18:38.461070Z", - "iopub.status.idle": "2023-08-07T00:18:38.463158Z", - "shell.execute_reply": "2023-08-07T00:18:38.462899Z" + "iopub.execute_input": "2023-08-21T02:29:02.045927Z", + "iopub.status.busy": "2023-08-21T02:29:02.045761Z", + "iopub.status.idle": "2023-08-21T02:29:02.047761Z", + "shell.execute_reply": "2023-08-21T02:29:02.047491Z" }, "lines_to_next_cell": 2 }, @@ -81,7 +81,7 @@ }, { "cell_type": "markdown", - "id": "713d30db", + "id": "9ac3acd5", "metadata": {}, "source": [ "## The Validation Set Approach\n", @@ -102,13 +102,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "99c95faf", + "id": "8af59641", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.464725Z", - "iopub.status.busy": "2023-08-07T00:18:38.464616Z", - "iopub.status.idle": "2023-08-07T00:18:38.472566Z", - "shell.execute_reply": "2023-08-07T00:18:38.472315Z" + "iopub.execute_input": "2023-08-21T02:29:02.049239Z", + "iopub.status.busy": "2023-08-21T02:29:02.049145Z", + "iopub.status.idle": "2023-08-21T02:29:02.055524Z", + "shell.execute_reply": "2023-08-21T02:29:02.055162Z" } }, "outputs": [], @@ -121,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "57be35df", + "id": "e76383f0", "metadata": {}, "source": [ "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`." @@ -130,13 +130,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "41b0717d", + "id": "d9b0b7c8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.474061Z", - "iopub.status.busy": "2023-08-07T00:18:38.473957Z", - "iopub.status.idle": "2023-08-07T00:18:38.477686Z", - "shell.execute_reply": "2023-08-07T00:18:38.477432Z" + "iopub.execute_input": "2023-08-21T02:29:02.057278Z", + "iopub.status.busy": "2023-08-21T02:29:02.057182Z", + "iopub.status.idle": "2023-08-21T02:29:02.062537Z", + "shell.execute_reply": "2023-08-21T02:29:02.062265Z" } }, "outputs": [], @@ -150,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "7f1bef95", + "id": "d196dd08", "metadata": {}, "source": [ "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n", @@ -160,13 +160,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "d7ea3c0d", + "id": "3e77d831", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.479141Z", - "iopub.status.busy": "2023-08-07T00:18:38.479053Z", - "iopub.status.idle": "2023-08-07T00:18:38.483270Z", - "shell.execute_reply": "2023-08-07T00:18:38.483037Z" + "iopub.execute_input": "2023-08-21T02:29:02.064056Z", + "iopub.status.busy": "2023-08-21T02:29:02.063966Z", + "iopub.status.idle": "2023-08-21T02:29:02.068279Z", + "shell.execute_reply": "2023-08-21T02:29:02.068024Z" } }, "outputs": [ @@ -190,7 +190,7 @@ }, { "cell_type": "markdown", - "id": "6dba5d55", + "id": "f4369ee6", "metadata": {}, "source": [ "Hence our estimate for the validation MSE of the linear regression\n", @@ -204,13 +204,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "a02a2d05", + "id": "0aa4bfcc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.484782Z", - "iopub.status.busy": "2023-08-07T00:18:38.484689Z", - "iopub.status.idle": "2023-08-07T00:18:38.486891Z", - "shell.execute_reply": "2023-08-07T00:18:38.486642Z" + "iopub.execute_input": "2023-08-21T02:29:02.069789Z", + "iopub.status.busy": "2023-08-21T02:29:02.069682Z", + "iopub.status.idle": "2023-08-21T02:29:02.071953Z", + "shell.execute_reply": "2023-08-21T02:29:02.071703Z" } }, "outputs": [], @@ -235,7 +235,7 @@ }, { "cell_type": "markdown", - "id": "39ab59b1", + "id": "0271dc50", "metadata": {}, "source": [ "Let’s use this function to estimate the validation MSE\n", @@ -247,13 +247,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "51d93dea", + "id": "a0dbd55f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.488297Z", - "iopub.status.busy": "2023-08-07T00:18:38.488205Z", - "iopub.status.idle": "2023-08-07T00:18:38.497955Z", - "shell.execute_reply": "2023-08-07T00:18:38.497708Z" + "iopub.execute_input": "2023-08-21T02:29:02.073322Z", + "iopub.status.busy": "2023-08-21T02:29:02.073229Z", + "iopub.status.idle": "2023-08-21T02:29:02.088464Z", + "shell.execute_reply": "2023-08-21T02:29:02.088192Z" } }, "outputs": [ @@ -280,7 +280,7 @@ }, { "cell_type": "markdown", - "id": "936e168a", + "id": "a7401536", "metadata": {}, "source": [ "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n", @@ -291,13 +291,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "83432f06", + "id": "885136a4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.499478Z", - "iopub.status.busy": "2023-08-07T00:18:38.499391Z", - "iopub.status.idle": "2023-08-07T00:18:38.509735Z", - "shell.execute_reply": "2023-08-07T00:18:38.509466Z" + "iopub.execute_input": "2023-08-21T02:29:02.089889Z", + "iopub.status.busy": "2023-08-21T02:29:02.089804Z", + "iopub.status.idle": "2023-08-21T02:29:02.105353Z", + "shell.execute_reply": "2023-08-21T02:29:02.105089Z" } }, "outputs": [ @@ -327,7 +327,7 @@ }, { "cell_type": "markdown", - "id": "f5ceb357", + "id": "00785402", "metadata": {}, "source": [ "Using this split of the observations into a training set and a validation set,\n", @@ -341,7 +341,7 @@ }, { "cell_type": "markdown", - "id": "6d624a5c", + "id": "21c071b8", "metadata": {}, "source": [ "## Cross-Validation\n", @@ -374,13 +374,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "bcfc433f", + "id": "6d957d8c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:38.511210Z", - "iopub.status.busy": "2023-08-07T00:18:38.511122Z", - "iopub.status.idle": "2023-08-07T00:18:39.069624Z", - "shell.execute_reply": "2023-08-07T00:18:39.069325Z" + "iopub.execute_input": "2023-08-21T02:29:02.106979Z", + "iopub.status.busy": "2023-08-21T02:29:02.106884Z", + "iopub.status.idle": "2023-08-21T02:29:03.184550Z", + "shell.execute_reply": "2023-08-21T02:29:03.184259Z" }, "lines_to_next_cell": 0 }, @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "5b0f6f30", + "id": "c17e2bc8", "metadata": {}, "source": [ "The arguments to `cross_validate()` are as follows: an\n", @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "b527f67f", + "id": "5c7901f2", "metadata": {}, "source": [ "We can repeat this procedure for increasingly complex polynomial fits.\n", @@ -442,13 +442,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "f951ffc8", + "id": "e2b5ce95", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:39.071240Z", - "iopub.status.busy": "2023-08-07T00:18:39.071138Z", - "iopub.status.idle": "2023-08-07T00:18:39.674084Z", - "shell.execute_reply": "2023-08-07T00:18:39.673774Z" + "iopub.execute_input": "2023-08-21T02:29:03.186226Z", + "iopub.status.busy": "2023-08-21T02:29:03.186108Z", + "iopub.status.idle": "2023-08-21T02:29:03.782413Z", + "shell.execute_reply": "2023-08-21T02:29:03.782122Z" }, "lines_to_next_cell": 0 }, @@ -480,7 +480,7 @@ }, { "cell_type": "markdown", - "id": "792f1304", + "id": "03706248", "metadata": {}, "source": [ "As in Figure 5.4, we see a sharp drop in the estimated test MSE between the linear and\n", @@ -499,13 +499,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "e3610b5a", + "id": "1dda1bd7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:18:39.675725Z", - "iopub.status.busy": "2023-08-07T00:18:39.675614Z", - "iopub.status.idle": "2023-08-07T00:18:39.678046Z", - "shell.execute_reply": "2023-08-07T00:18:39.677762Z" + "iopub.execute_input": "2023-08-21T02:29:03.783997Z", + "iopub.status.busy": "2023-08-21T02:29:03.783886Z", + "iopub.status.idle": "2023-08-21T02:29:03.786132Z", + "shell.execute_reply": "2023-08-21T02:29:03.785881Z" } }, "outputs": [ @@ -530,7 +530,7 @@ }, { "cell_type": "markdown", - "id": "983625b2", + "id": "f5092f1b", "metadata": {}, "source": [ "In the CV example above, we used $K=n$, but of course we can also use $K" ] @@ -907,7 +907,7 @@ }, { "cell_type": "markdown", - "id": "baeedcf4", + "id": "62207ca5", "metadata": {}, "source": [ "The variable `lstat` measures the percentage of individuals with\n", @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "bf4867c8", + "id": "361d7f84", "metadata": {}, "source": [ "Now we use the cross-validation function to see whether pruning\n", @@ -929,13 +929,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "9e563bf2", + "id": "33fb7786", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:16.498280Z", - "iopub.status.busy": "2023-08-07T00:19:16.498155Z", - "iopub.status.idle": "2023-08-07T00:19:16.543916Z", - "shell.execute_reply": "2023-08-07T00:19:16.543596Z" + "iopub.execute_input": "2023-08-21T02:29:46.706833Z", + "iopub.status.busy": "2023-08-21T02:29:46.706699Z", + "iopub.status.idle": "2023-08-21T02:29:46.756824Z", + "shell.execute_reply": "2023-08-21T02:29:46.756520Z" } }, "outputs": [], @@ -954,7 +954,7 @@ }, { "cell_type": "markdown", - "id": "de0ed389", + "id": "e7218495", "metadata": {}, "source": [ "In keeping with the cross-validation results, we use the pruned tree\n", @@ -964,13 +964,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "3a635c52", + "id": "a386755b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:16.545489Z", - "iopub.status.busy": "2023-08-07T00:19:16.545404Z", - "iopub.status.idle": "2023-08-07T00:19:16.548169Z", - "shell.execute_reply": "2023-08-07T00:19:16.547932Z" + "iopub.execute_input": "2023-08-21T02:29:46.758663Z", + "iopub.status.busy": "2023-08-21T02:29:46.758546Z", + "iopub.status.idle": "2023-08-21T02:29:46.761814Z", + "shell.execute_reply": "2023-08-21T02:29:46.761470Z" }, "lines_to_next_cell": 2 }, @@ -978,7 +978,7 @@ { "data": { "text/plain": [ - "28.069857549754044" + "28.06985754975404" ] }, "execution_count": 22, @@ -993,7 +993,7 @@ }, { "cell_type": "markdown", - "id": "41a34a36", + "id": "7fe01e33", "metadata": {}, "source": [ "In other words, the test set MSE associated with the regression tree\n", @@ -1010,20 +1010,20 @@ { "cell_type": "code", "execution_count": 23, - "id": "5542ece6", + "id": "3d646928", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:16.549690Z", - "iopub.status.busy": "2023-08-07T00:19:16.549586Z", - "iopub.status.idle": "2023-08-07T00:19:16.832898Z", - "shell.execute_reply": "2023-08-07T00:19:16.832568Z" + "iopub.execute_input": "2023-08-21T02:29:46.763419Z", + "iopub.status.busy": "2023-08-21T02:29:46.763331Z", + "iopub.status.idle": "2023-08-21T02:29:47.049615Z", + "shell.execute_reply": "2023-08-21T02:29:47.049265Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1041,7 +1041,7 @@ }, { "cell_type": "markdown", - "id": "817eff2c", + "id": "074e230c", "metadata": {}, "source": [ " \n", @@ -1050,7 +1050,7 @@ }, { "cell_type": "markdown", - "id": "3e4b98e4", + "id": "aa0e4849", "metadata": {}, "source": [ "## Bagging and Random Forests" @@ -1058,7 +1058,7 @@ }, { "cell_type": "markdown", - "id": "878b4a89", + "id": "b17ca840", "metadata": {}, "source": [ "Here we apply bagging and random forests to the `Boston` data, using\n", @@ -1071,13 +1071,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "c82eb98b", + "id": "275feef7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:16.834792Z", - "iopub.status.busy": "2023-08-07T00:19:16.834681Z", - "iopub.status.idle": "2023-08-07T00:19:16.995212Z", - "shell.execute_reply": "2023-08-07T00:19:16.994915Z" + "iopub.execute_input": "2023-08-21T02:29:47.051447Z", + "iopub.status.busy": "2023-08-21T02:29:47.051323Z", + "iopub.status.idle": "2023-08-21T02:29:47.211049Z", + "shell.execute_reply": "2023-08-21T02:29:47.210740Z" }, "lines_to_next_cell": 2 }, @@ -1103,7 +1103,7 @@ }, { "cell_type": "markdown", - "id": "06cf548a", + "id": "06e84da6", "metadata": {}, "source": [ "The argument `max_features` indicates that all 12 predictors should\n", @@ -1115,13 +1115,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "7d592004", + "id": "01dbbef3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:16.996857Z", - "iopub.status.busy": "2023-08-07T00:19:16.996747Z", - "iopub.status.idle": "2023-08-07T00:19:17.088613Z", - "shell.execute_reply": "2023-08-07T00:19:17.088204Z" + "iopub.execute_input": "2023-08-21T02:29:47.212716Z", + "iopub.status.busy": "2023-08-21T02:29:47.212598Z", + "iopub.status.idle": "2023-08-21T02:29:47.305563Z", + "shell.execute_reply": "2023-08-21T02:29:47.305155Z" } }, "outputs": [ @@ -1155,7 +1155,7 @@ }, { "cell_type": "markdown", - "id": "f38f7522", + "id": "885a3843", "metadata": {}, "source": [ "The test set MSE associated with the bagged regression tree is\n", @@ -1168,13 +1168,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "892aa556", + "id": "b75cc90e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:17.090496Z", - "iopub.status.busy": "2023-08-07T00:19:17.090359Z", - "iopub.status.idle": "2023-08-07T00:19:17.888986Z", - "shell.execute_reply": "2023-08-07T00:19:17.888683Z" + "iopub.execute_input": "2023-08-21T02:29:47.307949Z", + "iopub.status.busy": "2023-08-21T02:29:47.307809Z", + "iopub.status.idle": "2023-08-21T02:29:48.296516Z", + "shell.execute_reply": "2023-08-21T02:29:48.296211Z" }, "lines_to_next_cell": 0 }, @@ -1200,7 +1200,7 @@ }, { "cell_type": "markdown", - "id": "6c2a8868", + "id": "37d5fe61", "metadata": {}, "source": [ "There is not much change. Bagging and random forests cannot overfit by\n", @@ -1217,13 +1217,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "35636d87", + "id": "bf9a5ed4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:17.890621Z", - "iopub.status.busy": "2023-08-07T00:19:17.890501Z", - "iopub.status.idle": "2023-08-07T00:19:17.998521Z", - "shell.execute_reply": "2023-08-07T00:19:17.998227Z" + "iopub.execute_input": "2023-08-21T02:29:48.298333Z", + "iopub.status.busy": "2023-08-21T02:29:48.298184Z", + "iopub.status.idle": "2023-08-21T02:29:48.412949Z", + "shell.execute_reply": "2023-08-21T02:29:48.412594Z" }, "lines_to_next_cell": 2 }, @@ -1248,7 +1248,7 @@ }, { "cell_type": "markdown", - "id": "a6edace1", + "id": "eb2c3917", "metadata": {}, "source": [ "The test set MSE is 20.04;\n", @@ -1260,13 +1260,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "b5a0869f", + "id": "71316e9a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:18.000185Z", - "iopub.status.busy": "2023-08-07T00:19:18.000073Z", - "iopub.status.idle": "2023-08-07T00:19:18.006477Z", - "shell.execute_reply": "2023-08-07T00:19:18.006110Z" + "iopub.execute_input": "2023-08-21T02:29:48.414933Z", + "iopub.status.busy": "2023-08-21T02:29:48.414785Z", + "iopub.status.idle": "2023-08-21T02:29:48.422251Z", + "shell.execute_reply": "2023-08-21T02:29:48.421922Z" }, "lines_to_next_cell": 0 }, @@ -1378,7 +1378,7 @@ }, { "cell_type": "markdown", - "id": "4acf5bdc", + "id": "b3cd6a28", "metadata": {}, "source": [ " This\n", @@ -1394,7 +1394,7 @@ }, { "cell_type": "markdown", - "id": "9a07b01d", + "id": "270fc0fb", "metadata": {}, "source": [ "## Boosting" @@ -1402,7 +1402,7 @@ }, { "cell_type": "markdown", - "id": "f2b3dde2", + "id": "5bd28754", "metadata": {}, "source": [ "Here we use `GradientBoostingRegressor()` from `sklearn.ensemble`\n", @@ -1418,13 +1418,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "844fce26", + "id": "0bcc5ff1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:18.008149Z", - "iopub.status.busy": "2023-08-07T00:19:18.008037Z", - "iopub.status.idle": "2023-08-07T00:19:21.749934Z", - "shell.execute_reply": "2023-08-07T00:19:21.749526Z" + "iopub.execute_input": "2023-08-21T02:29:48.424109Z", + "iopub.status.busy": "2023-08-21T02:29:48.423996Z", + "iopub.status.idle": "2023-08-21T02:29:52.311829Z", + "shell.execute_reply": "2023-08-21T02:29:52.311492Z" } }, "outputs": [ @@ -1455,7 +1455,7 @@ }, { "cell_type": "markdown", - "id": "15f35667", + "id": "4e8553ec", "metadata": {}, "source": [ "We can see how the training error decreases with the `train_score_` attribute.\n", @@ -1466,13 +1466,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "eae8687c", + "id": "060f47eb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:21.751640Z", - "iopub.status.busy": "2023-08-07T00:19:21.751529Z", - "iopub.status.idle": "2023-08-07T00:19:22.217916Z", - "shell.execute_reply": "2023-08-07T00:19:22.217584Z" + "iopub.execute_input": "2023-08-21T02:29:52.313643Z", + "iopub.status.busy": "2023-08-21T02:29:52.313522Z", + "iopub.status.idle": "2023-08-21T02:29:52.788145Z", + "shell.execute_reply": "2023-08-21T02:29:52.787823Z" } }, "outputs": [ @@ -1507,7 +1507,7 @@ }, { "cell_type": "markdown", - "id": "e0499e04", + "id": "3ebd0944", "metadata": {}, "source": [ "We now use the boosted model to predict `medv` on the test set:" @@ -1516,13 +1516,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "46947fc6", + "id": "43505dad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:22.219939Z", - "iopub.status.busy": "2023-08-07T00:19:22.219785Z", - "iopub.status.idle": "2023-08-07T00:19:22.232084Z", - "shell.execute_reply": "2023-08-07T00:19:22.231779Z" + "iopub.execute_input": "2023-08-21T02:29:52.789753Z", + "iopub.status.busy": "2023-08-21T02:29:52.789632Z", + "iopub.status.idle": "2023-08-21T02:29:52.801529Z", + "shell.execute_reply": "2023-08-21T02:29:52.801234Z" } }, "outputs": [ @@ -1544,7 +1544,7 @@ }, { "cell_type": "markdown", - "id": "07d3ccd4", + "id": "7f76bb89", "metadata": {}, "source": [ " The test MSE obtained is 14.48,\n", @@ -1557,13 +1557,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "f67fdfec", + "id": "c0a03126", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:22.233692Z", - "iopub.status.busy": "2023-08-07T00:19:22.233584Z", - "iopub.status.idle": "2023-08-07T00:19:24.675084Z", - "shell.execute_reply": "2023-08-07T00:19:24.674626Z" + "iopub.execute_input": "2023-08-21T02:29:52.803315Z", + "iopub.status.busy": "2023-08-21T02:29:52.803174Z", + "iopub.status.idle": "2023-08-21T02:29:55.330726Z", + "shell.execute_reply": "2023-08-21T02:29:55.330425Z" }, "lines_to_next_cell": 2 }, @@ -1592,7 +1592,7 @@ }, { "cell_type": "markdown", - "id": "f767c57a", + "id": "cbcad91c", "metadata": {}, "source": [ "In this case, using $\\lambda=0.2$ leads to a almost the same test MSE\n", @@ -1603,7 +1603,7 @@ }, { "cell_type": "markdown", - "id": "37cf0481", + "id": "8d561719", "metadata": {}, "source": [ "## Bayesian Additive Regression Trees" @@ -1611,7 +1611,7 @@ }, { "cell_type": "markdown", - "id": "c3a3419a", + "id": "f68c0d8f", "metadata": {}, "source": [ "In this section we demonstrate a `Python` implementation of BART found in the\n", @@ -1624,13 +1624,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "06b92877", + "id": "58f6e11f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:24.676878Z", - "iopub.status.busy": "2023-08-07T00:19:24.676742Z", - "iopub.status.idle": "2023-08-07T00:19:26.416295Z", - "shell.execute_reply": "2023-08-07T00:19:26.415996Z" + "iopub.execute_input": "2023-08-21T02:29:55.332677Z", + "iopub.status.busy": "2023-08-21T02:29:55.332550Z", + "iopub.status.idle": "2023-08-21T02:29:56.797204Z", + "shell.execute_reply": "2023-08-21T02:29:56.796899Z" }, "lines_to_next_cell": 2 }, @@ -1656,7 +1656,7 @@ }, { "cell_type": "markdown", - "id": "d6d7ad70", + "id": "26e4d574", "metadata": {}, "source": [ "On this data set, with this split into test and training, we see that the test error of BART is similar to that of random forest." @@ -1665,13 +1665,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "c09cb099", + "id": "a5b1296f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:26.418098Z", - "iopub.status.busy": "2023-08-07T00:19:26.417976Z", - "iopub.status.idle": "2023-08-07T00:19:26.718709Z", - "shell.execute_reply": "2023-08-07T00:19:26.718412Z" + "iopub.execute_input": "2023-08-21T02:29:56.798892Z", + "iopub.status.busy": "2023-08-21T02:29:56.798779Z", + "iopub.status.idle": "2023-08-21T02:29:57.098494Z", + "shell.execute_reply": "2023-08-21T02:29:57.098140Z" }, "lines_to_next_cell": 2 }, @@ -1694,7 +1694,7 @@ }, { "cell_type": "markdown", - "id": "bb48211a", + "id": "92a60f97", "metadata": {}, "source": [ "We can check how many times each variable appeared in the collection of trees.\n", @@ -1704,13 +1704,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "f9d6b394", + "id": "36c52755", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:26.720402Z", - "iopub.status.busy": "2023-08-07T00:19:26.720254Z", - "iopub.status.idle": "2023-08-07T00:19:26.723384Z", - "shell.execute_reply": "2023-08-07T00:19:26.723106Z" + "iopub.execute_input": "2023-08-21T02:29:57.100444Z", + "iopub.status.busy": "2023-08-21T02:29:57.100310Z", + "iopub.status.idle": "2023-08-21T02:29:57.103577Z", + "shell.execute_reply": "2023-08-21T02:29:57.103213Z" }, "lines_to_next_cell": 0 }, @@ -1746,7 +1746,7 @@ }, { "cell_type": "markdown", - "id": "0aac2d50", + "id": "ab69e5b5", "metadata": {}, "source": [ " \n", diff --git a/Ch09-svm-lab.ipynb b/Ch09-svm-lab.ipynb index c6863a2..bf1cc7c 100644 --- a/Ch09-svm-lab.ipynb +++ b/Ch09-svm-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d45c6d2b", + "id": "30b873f3", "metadata": {}, "source": [ "\n", @@ -12,7 +12,7 @@ }, { "cell_type": "markdown", - "id": "94d1e03c", + "id": "3e06bca8", "metadata": {}, "source": [ "# Lab: Support Vector Machines\n", @@ -25,13 +25,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "eeaa5be0", + "id": "3973b95f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:27.947789Z", - "iopub.status.busy": "2023-08-07T00:19:27.947634Z", - "iopub.status.idle": "2023-08-07T00:19:28.991210Z", - "shell.execute_reply": "2023-08-07T00:19:28.990616Z" + "iopub.execute_input": "2023-08-21T02:29:58.477582Z", + "iopub.status.busy": "2023-08-21T02:29:58.477467Z", + "iopub.status.idle": "2023-08-21T02:29:59.432527Z", + "shell.execute_reply": "2023-08-21T02:29:59.432225Z" }, "lines_to_next_cell": 0 }, @@ -45,7 +45,7 @@ }, { "cell_type": "markdown", - "id": "26ebd377", + "id": "631b7d3d", "metadata": {}, "source": [ "We also collect the new imports\n", @@ -55,13 +55,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "41a59634", + "id": "0161e55e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:28.993557Z", - "iopub.status.busy": "2023-08-07T00:19:28.993245Z", - "iopub.status.idle": "2023-08-07T00:19:29.028199Z", - "shell.execute_reply": "2023-08-07T00:19:29.027857Z" + "iopub.execute_input": "2023-08-21T02:29:59.434432Z", + "iopub.status.busy": "2023-08-21T02:29:59.434258Z", + "iopub.status.idle": "2023-08-21T02:29:59.466972Z", + "shell.execute_reply": "2023-08-21T02:29:59.466647Z" } }, "outputs": [], @@ -73,7 +73,7 @@ }, { "cell_type": "markdown", - "id": "f197b846", + "id": "b397af05", "metadata": {}, "source": [ "We will use the function `RocCurveDisplay.from_estimator()` to\n", @@ -83,13 +83,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "c9a175d7", + "id": "7661b056", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.030225Z", - "iopub.status.busy": "2023-08-07T00:19:29.030097Z", - "iopub.status.idle": "2023-08-07T00:19:29.032026Z", - "shell.execute_reply": "2023-08-07T00:19:29.031756Z" + "iopub.execute_input": "2023-08-21T02:29:59.469128Z", + "iopub.status.busy": "2023-08-21T02:29:59.468999Z", + "iopub.status.idle": "2023-08-21T02:29:59.470961Z", + "shell.execute_reply": "2023-08-21T02:29:59.470667Z" } }, "outputs": [], @@ -99,7 +99,7 @@ }, { "cell_type": "markdown", - "id": "f666c212", + "id": "38115984", "metadata": {}, "source": [ "## Support Vector Classifier\n", @@ -123,13 +123,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "a7216b47", + "id": "46e9ab84", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.033695Z", - "iopub.status.busy": "2023-08-07T00:19:29.033581Z", - "iopub.status.idle": "2023-08-07T00:19:29.207161Z", - "shell.execute_reply": "2023-08-07T00:19:29.205980Z" + "iopub.execute_input": "2023-08-21T02:29:59.472867Z", + "iopub.status.busy": "2023-08-21T02:29:59.472726Z", + "iopub.status.idle": "2023-08-21T02:29:59.583508Z", + "shell.execute_reply": "2023-08-21T02:29:59.583126Z" }, "lines_to_next_cell": 0 }, @@ -159,7 +159,7 @@ }, { "cell_type": "markdown", - "id": "7b4aff06", + "id": "a9766d18", "metadata": {}, "source": [ "They are not. We now fit the classifier." @@ -168,13 +168,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "ed329198", + "id": "605ffdc0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.211951Z", - "iopub.status.busy": "2023-08-07T00:19:29.211403Z", - "iopub.status.idle": "2023-08-07T00:19:29.220643Z", - "shell.execute_reply": "2023-08-07T00:19:29.219858Z" + "iopub.execute_input": "2023-08-21T02:29:59.585485Z", + "iopub.status.busy": "2023-08-21T02:29:59.585317Z", + "iopub.status.idle": "2023-08-21T02:29:59.590274Z", + "shell.execute_reply": "2023-08-21T02:29:59.589979Z" }, "lines_to_next_cell": 2 }, @@ -200,7 +200,7 @@ }, { "cell_type": "markdown", - "id": "5e6b4c79", + "id": "16215b77", "metadata": {}, "source": [ "The support vector classifier with two features can\n", @@ -212,13 +212,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "95494b8b", + "id": "302a49a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.224179Z", - "iopub.status.busy": "2023-08-07T00:19:29.223775Z", - "iopub.status.idle": "2023-08-07T00:19:29.400927Z", - "shell.execute_reply": "2023-08-07T00:19:29.400620Z" + "iopub.execute_input": "2023-08-21T02:29:59.591976Z", + "iopub.status.busy": "2023-08-21T02:29:59.591865Z", + "iopub.status.idle": "2023-08-21T02:29:59.734225Z", + "shell.execute_reply": "2023-08-21T02:29:59.733936Z" } }, "outputs": [ @@ -243,7 +243,7 @@ }, { "cell_type": "markdown", - "id": "f6ce1246", + "id": "6010e865", "metadata": {}, "source": [ "The decision\n", @@ -257,13 +257,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "98c2236f", + "id": "cc1d6a13", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.402894Z", - "iopub.status.busy": "2023-08-07T00:19:29.402744Z", - "iopub.status.idle": "2023-08-07T00:19:29.544636Z", - "shell.execute_reply": "2023-08-07T00:19:29.544249Z" + "iopub.execute_input": "2023-08-21T02:29:59.735943Z", + "iopub.status.busy": "2023-08-21T02:29:59.735816Z", + "iopub.status.idle": "2023-08-21T02:29:59.878335Z", + "shell.execute_reply": "2023-08-21T02:29:59.878032Z" }, "lines_to_next_cell": 0 }, @@ -291,7 +291,7 @@ }, { "cell_type": "markdown", - "id": "906f4bb8", + "id": "301d764d", "metadata": {}, "source": [ "With a smaller value of the cost parameter, we\n", @@ -303,13 +303,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "b498f594", + "id": "6133c846", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.546722Z", - "iopub.status.busy": "2023-08-07T00:19:29.546549Z", - "iopub.status.idle": "2023-08-07T00:19:29.549088Z", - "shell.execute_reply": "2023-08-07T00:19:29.548814Z" + "iopub.execute_input": "2023-08-21T02:29:59.880078Z", + "iopub.status.busy": "2023-08-21T02:29:59.879965Z", + "iopub.status.idle": "2023-08-21T02:29:59.882347Z", + "shell.execute_reply": "2023-08-21T02:29:59.882070Z" }, "lines_to_next_cell": 2 }, @@ -331,7 +331,7 @@ }, { "cell_type": "markdown", - "id": "90a0ee53", + "id": "0693b3eb", "metadata": {}, "source": [ "Since the support vector machine is an estimator in `sklearn`, we\n", @@ -341,13 +341,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "b65e80d6", + "id": "9adb3793", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.550593Z", - "iopub.status.busy": "2023-08-07T00:19:29.550485Z", - "iopub.status.idle": "2023-08-07T00:19:29.578952Z", - "shell.execute_reply": "2023-08-07T00:19:29.578657Z" + "iopub.execute_input": "2023-08-21T02:29:59.883852Z", + "iopub.status.busy": "2023-08-21T02:29:59.883749Z", + "iopub.status.idle": "2023-08-21T02:29:59.910535Z", + "shell.execute_reply": "2023-08-21T02:29:59.910272Z" }, "lines_to_next_cell": 2 }, @@ -378,7 +378,7 @@ }, { "cell_type": "markdown", - "id": "d390528c", + "id": "611e76a6", "metadata": {}, "source": [ "We can easily access the cross-validation errors for each of these models\n", @@ -389,13 +389,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "bba8fad7", + "id": "d3ab343e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.580977Z", - "iopub.status.busy": "2023-08-07T00:19:29.580845Z", - "iopub.status.idle": "2023-08-07T00:19:29.583558Z", - "shell.execute_reply": "2023-08-07T00:19:29.583239Z" + "iopub.execute_input": "2023-08-21T02:29:59.912005Z", + "iopub.status.busy": "2023-08-21T02:29:59.911925Z", + "iopub.status.idle": "2023-08-21T02:29:59.914189Z", + "shell.execute_reply": "2023-08-21T02:29:59.913943Z" }, "lines_to_next_cell": 0 }, @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "703e2d43", + "id": "41d85a2a", "metadata": {}, "source": [ "We see that `C=1` results in the highest cross-validation\n", @@ -430,13 +430,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "ad64269d", + "id": "6aba117e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.585087Z", - "iopub.status.busy": "2023-08-07T00:19:29.584981Z", - "iopub.status.idle": "2023-08-07T00:19:29.586995Z", - "shell.execute_reply": "2023-08-07T00:19:29.586714Z" + "iopub.execute_input": "2023-08-21T02:29:59.915563Z", + "iopub.status.busy": "2023-08-21T02:29:59.915487Z", + "iopub.status.idle": "2023-08-21T02:29:59.917323Z", + "shell.execute_reply": "2023-08-21T02:29:59.917078Z" } }, "outputs": [], @@ -448,7 +448,7 @@ }, { "cell_type": "markdown", - "id": "db41f5e2", + "id": "ddbda9de", "metadata": {}, "source": [ "Now we predict the class labels of these test observations. Here we\n", @@ -459,13 +459,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "5107fca1", + "id": "dbe7d737", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.588685Z", - "iopub.status.busy": "2023-08-07T00:19:29.588519Z", - "iopub.status.idle": "2023-08-07T00:19:29.595768Z", - "shell.execute_reply": "2023-08-07T00:19:29.595341Z" + "iopub.execute_input": "2023-08-21T02:29:59.918744Z", + "iopub.status.busy": "2023-08-21T02:29:59.918666Z", + "iopub.status.idle": "2023-08-21T02:29:59.925361Z", + "shell.execute_reply": "2023-08-21T02:29:59.925039Z" } }, "outputs": [ @@ -534,7 +534,7 @@ }, { "cell_type": "markdown", - "id": "bbfc8005", + "id": "7f002ea6", "metadata": {}, "source": [ "Thus, with this value of `C`,\n", @@ -546,13 +546,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "0320d9e0", + "id": "ab1697c2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.597509Z", - "iopub.status.busy": "2023-08-07T00:19:29.597387Z", - "iopub.status.idle": "2023-08-07T00:19:29.602346Z", - "shell.execute_reply": "2023-08-07T00:19:29.601964Z" + "iopub.execute_input": "2023-08-21T02:29:59.927158Z", + "iopub.status.busy": "2023-08-21T02:29:59.927027Z", + "iopub.status.idle": "2023-08-21T02:29:59.931558Z", + "shell.execute_reply": "2023-08-21T02:29:59.931228Z" } }, "outputs": [ @@ -622,7 +622,7 @@ }, { "cell_type": "markdown", - "id": "427d775f", + "id": "7574703a", "metadata": {}, "source": [ "In this case 60% of test observations are correctly classified.\n", @@ -637,13 +637,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "84d7e778", + "id": "0fd42b1e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.604018Z", - "iopub.status.busy": "2023-08-07T00:19:29.603879Z", - "iopub.status.idle": "2023-08-07T00:19:29.734586Z", - "shell.execute_reply": "2023-08-07T00:19:29.734264Z" + "iopub.execute_input": "2023-08-21T02:29:59.933100Z", + "iopub.status.busy": "2023-08-21T02:29:59.933001Z", + "iopub.status.idle": "2023-08-21T02:30:00.054738Z", + "shell.execute_reply": "2023-08-21T02:30:00.054338Z" } }, "outputs": [ @@ -666,7 +666,7 @@ }, { "cell_type": "markdown", - "id": "ff7bdad1", + "id": "4bdaf415", "metadata": {}, "source": [ "Now the observations are just barely linearly separable." @@ -675,13 +675,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "abb1f8be", + "id": "09c15299", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.736388Z", - "iopub.status.busy": "2023-08-07T00:19:29.736251Z", - "iopub.status.idle": "2023-08-07T00:19:29.741179Z", - "shell.execute_reply": "2023-08-07T00:19:29.740886Z" + "iopub.execute_input": "2023-08-21T02:30:00.056655Z", + "iopub.status.busy": "2023-08-21T02:30:00.056526Z", + "iopub.status.idle": "2023-08-21T02:30:00.061096Z", + "shell.execute_reply": "2023-08-21T02:30:00.060792Z" } }, "outputs": [ @@ -750,7 +750,7 @@ }, { "cell_type": "markdown", - "id": "c44297cc", + "id": "d987eecc", "metadata": {}, "source": [ "We fit the\n", @@ -762,13 +762,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "2e4ed2f5", + "id": "d5fd2ff9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.742864Z", - "iopub.status.busy": "2023-08-07T00:19:29.742750Z", - "iopub.status.idle": "2023-08-07T00:19:29.860686Z", - "shell.execute_reply": "2023-08-07T00:19:29.860305Z" + "iopub.execute_input": "2023-08-21T02:30:00.062673Z", + "iopub.status.busy": "2023-08-21T02:30:00.062585Z", + "iopub.status.idle": "2023-08-21T02:30:00.199860Z", + "shell.execute_reply": "2023-08-21T02:30:00.199129Z" }, "lines_to_next_cell": 0 }, @@ -794,7 +794,7 @@ }, { "cell_type": "markdown", - "id": "2836d70d", + "id": "0834d471", "metadata": {}, "source": [ "Indeed no training errors were made and only three support vectors were used.\n", @@ -807,13 +807,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "164a611c", + "id": "39aff1b1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.862647Z", - "iopub.status.busy": "2023-08-07T00:19:29.862496Z", - "iopub.status.idle": "2023-08-07T00:19:29.867261Z", - "shell.execute_reply": "2023-08-07T00:19:29.866916Z" + "iopub.execute_input": "2023-08-21T02:30:00.202380Z", + "iopub.status.busy": "2023-08-21T02:30:00.202233Z", + "iopub.status.idle": "2023-08-21T02:30:00.207886Z", + "shell.execute_reply": "2023-08-21T02:30:00.207493Z" } }, "outputs": [ @@ -882,7 +882,7 @@ }, { "cell_type": "markdown", - "id": "39a432d1", + "id": "d0684844", "metadata": {}, "source": [ "Using `C=0.1`, we again do not misclassify any training observations, but we\n", @@ -894,13 +894,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "c67591a1", + "id": "63a9d752", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.868821Z", - "iopub.status.busy": "2023-08-07T00:19:29.868723Z", - "iopub.status.idle": "2023-08-07T00:19:29.990207Z", - "shell.execute_reply": "2023-08-07T00:19:29.989921Z" + "iopub.execute_input": "2023-08-21T02:30:00.209907Z", + "iopub.status.busy": "2023-08-21T02:30:00.209781Z", + "iopub.status.idle": "2023-08-21T02:30:00.340803Z", + "shell.execute_reply": "2023-08-21T02:30:00.340433Z" }, "lines_to_next_cell": 2 }, @@ -926,7 +926,7 @@ }, { "cell_type": "markdown", - "id": "25e61f65", + "id": "a70d84f4", "metadata": {}, "source": [ "## Support Vector Machine\n", @@ -947,13 +947,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "322be574", + "id": "2fee8df5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.991910Z", - "iopub.status.busy": "2023-08-07T00:19:29.991799Z", - "iopub.status.idle": "2023-08-07T00:19:29.993907Z", - "shell.execute_reply": "2023-08-07T00:19:29.993635Z" + "iopub.execute_input": "2023-08-21T02:30:00.342773Z", + "iopub.status.busy": "2023-08-21T02:30:00.342626Z", + "iopub.status.idle": "2023-08-21T02:30:00.345094Z", + "shell.execute_reply": "2023-08-21T02:30:00.344774Z" } }, "outputs": [], @@ -966,7 +966,7 @@ }, { "cell_type": "markdown", - "id": "22fe2182", + "id": "d5c7545e", "metadata": {}, "source": [ "Plotting the data makes it clear that the class boundary is indeed non-linear." @@ -975,13 +975,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "04fda182", + "id": "48f01abe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:29.995558Z", - "iopub.status.busy": "2023-08-07T00:19:29.995406Z", - "iopub.status.idle": "2023-08-07T00:19:30.089596Z", - "shell.execute_reply": "2023-08-07T00:19:30.089130Z" + "iopub.execute_input": "2023-08-21T02:30:00.347053Z", + "iopub.status.busy": "2023-08-21T02:30:00.346902Z", + "iopub.status.idle": "2023-08-21T02:30:00.440453Z", + "shell.execute_reply": "2023-08-21T02:30:00.440153Z" }, "lines_to_next_cell": 2 }, @@ -989,7 +989,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -1017,7 +1017,7 @@ }, { "cell_type": "markdown", - "id": "64913fe3", + "id": "7c0bc32b", "metadata": {}, "source": [ "The data is randomly split into training and testing groups. We then\n", @@ -1028,13 +1028,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "0c2690d1", + "id": "4acc3246", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:30.091605Z", - "iopub.status.busy": "2023-08-07T00:19:30.091498Z", - "iopub.status.idle": "2023-08-07T00:19:30.095614Z", - "shell.execute_reply": "2023-08-07T00:19:30.095347Z" + "iopub.execute_input": "2023-08-21T02:30:00.442257Z", + "iopub.status.busy": "2023-08-21T02:30:00.442156Z", + "iopub.status.idle": "2023-08-21T02:30:00.446674Z", + "shell.execute_reply": "2023-08-21T02:30:00.446369Z" } }, "outputs": [ @@ -1066,7 +1066,7 @@ }, { "cell_type": "markdown", - "id": "5da9efdb", + "id": "b2c7e95e", "metadata": {}, "source": [ "The plot shows that the resulting SVM has a decidedly non-linear\n", @@ -1076,13 +1076,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "3eb171e8", + "id": "e9852a28", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:30.097178Z", - "iopub.status.busy": "2023-08-07T00:19:30.097088Z", - "iopub.status.idle": "2023-08-07T00:19:30.357131Z", - "shell.execute_reply": "2023-08-07T00:19:30.356847Z" + "iopub.execute_input": "2023-08-21T02:30:00.448268Z", + "iopub.status.busy": "2023-08-21T02:30:00.448160Z", + "iopub.status.idle": "2023-08-21T02:30:00.828511Z", + "shell.execute_reply": "2023-08-21T02:30:00.828128Z" } }, "outputs": [ @@ -1107,7 +1107,7 @@ }, { "cell_type": "markdown", - "id": "ab5b1446", + "id": "acfa4bed", "metadata": {}, "source": [ "We can see from the figure that there are a fair number of training\n", @@ -1120,13 +1120,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "9a6b905b", + "id": "01232fc9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:30.358811Z", - "iopub.status.busy": "2023-08-07T00:19:30.358698Z", - "iopub.status.idle": "2023-08-07T00:19:30.513702Z", - "shell.execute_reply": "2023-08-07T00:19:30.513395Z" + "iopub.execute_input": "2023-08-21T02:30:00.830365Z", + "iopub.status.busy": "2023-08-21T02:30:00.830226Z", + "iopub.status.idle": "2023-08-21T02:30:01.132677Z", + "shell.execute_reply": "2023-08-21T02:30:01.132224Z" } }, "outputs": [ @@ -1153,7 +1153,7 @@ }, { "cell_type": "markdown", - "id": "300c1b8b", + "id": "5bc77e3f", "metadata": {}, "source": [ "We can perform cross-validation using `skm.GridSearchCV()` to select the\n", @@ -1164,13 +1164,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "5ab01d6c", + "id": "bcbd15a4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:30.515803Z", - "iopub.status.busy": "2023-08-07T00:19:30.515668Z", - "iopub.status.idle": "2023-08-07T00:19:30.612245Z", - "shell.execute_reply": "2023-08-07T00:19:30.611940Z" + "iopub.execute_input": "2023-08-21T02:30:01.134616Z", + "iopub.status.busy": "2023-08-21T02:30:01.134486Z", + "iopub.status.idle": "2023-08-21T02:30:01.243519Z", + "shell.execute_reply": "2023-08-21T02:30:01.243203Z" } }, "outputs": [ @@ -1201,7 +1201,7 @@ }, { "cell_type": "markdown", - "id": "1bb987ae", + "id": "997bbfbd", "metadata": {}, "source": [ "The best choice of parameters under five-fold CV is achieved at `C=1`\n", @@ -1212,13 +1212,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "166a6acb", + "id": "28ca551e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:30.614152Z", - "iopub.status.busy": "2023-08-07T00:19:30.614029Z", - "iopub.status.idle": "2023-08-07T00:19:30.850984Z", - "shell.execute_reply": "2023-08-07T00:19:30.850653Z" + "iopub.execute_input": "2023-08-21T02:30:01.245550Z", + "iopub.status.busy": "2023-08-21T02:30:01.245377Z", + "iopub.status.idle": "2023-08-21T02:30:01.600896Z", + "shell.execute_reply": "2023-08-21T02:30:01.600574Z" } }, "outputs": [ @@ -1303,7 +1303,7 @@ }, { "cell_type": "markdown", - "id": "39ee6f32", + "id": "48e671f4", "metadata": {}, "source": [ "With these parameters, 12% of test\n", @@ -1312,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "f0ea699d", + "id": "eaed0a87", "metadata": {}, "source": [ "## ROC Curves\n", @@ -1346,13 +1346,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "0607fc41", + "id": "68ac9421", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:30.853079Z", - "iopub.status.busy": "2023-08-07T00:19:30.852934Z", - "iopub.status.idle": "2023-08-07T00:19:30.948570Z", - "shell.execute_reply": "2023-08-07T00:19:30.948252Z" + "iopub.execute_input": "2023-08-21T02:30:01.602740Z", + "iopub.status.busy": "2023-08-21T02:30:01.602614Z", + "iopub.status.idle": "2023-08-21T02:30:01.698620Z", + "shell.execute_reply": "2023-08-21T02:30:01.698322Z" }, "lines_to_next_cell": 0 }, @@ -1380,7 +1380,7 @@ }, { "cell_type": "markdown", - "id": "54446e71", + "id": "0c35d32a", "metadata": {}, "source": [ " In this example, the SVM appears to provide accurate predictions. By increasing\n", @@ -1391,13 +1391,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "5211a882", + "id": "f79a9e0a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:30.950213Z", - "iopub.status.busy": "2023-08-07T00:19:30.950106Z", - "iopub.status.idle": "2023-08-07T00:19:31.095103Z", - "shell.execute_reply": "2023-08-07T00:19:31.094737Z" + "iopub.execute_input": "2023-08-21T02:30:01.700479Z", + "iopub.status.busy": "2023-08-21T02:30:01.700347Z", + "iopub.status.idle": "2023-08-21T02:30:01.837479Z", + "shell.execute_reply": "2023-08-21T02:30:01.837102Z" } }, "outputs": [ @@ -1428,7 +1428,7 @@ }, { "cell_type": "markdown", - "id": "de7e4be8", + "id": "7bd1a22b", "metadata": {}, "source": [ "However, these ROC curves are all on the training data. We are really\n", @@ -1440,13 +1440,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "12acc4ff", + "id": "bdb9e503", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:31.096951Z", - "iopub.status.busy": "2023-08-07T00:19:31.096805Z", - "iopub.status.idle": "2023-08-07T00:19:31.101372Z", - "shell.execute_reply": "2023-08-07T00:19:31.101097Z" + "iopub.execute_input": "2023-08-21T02:30:01.839390Z", + "iopub.status.busy": "2023-08-21T02:30:01.839243Z", + "iopub.status.idle": "2023-08-21T02:30:01.843595Z", + "shell.execute_reply": "2023-08-21T02:30:01.843287Z" } }, "outputs": [], @@ -1462,7 +1462,7 @@ }, { "cell_type": "markdown", - "id": "eb5c8aeb", + "id": "8a42e924", "metadata": {}, "source": [ "Let’s look at our tuned SVM." @@ -1471,13 +1471,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "21c81913", + "id": "329f5d2c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:31.103089Z", - "iopub.status.busy": "2023-08-07T00:19:31.102993Z", - "iopub.status.idle": "2023-08-07T00:19:31.204133Z", - "shell.execute_reply": "2023-08-07T00:19:31.203835Z" + "iopub.execute_input": "2023-08-21T02:30:01.845300Z", + "iopub.status.busy": "2023-08-21T02:30:01.845201Z", + "iopub.status.idle": "2023-08-21T02:30:01.944073Z", + "shell.execute_reply": "2023-08-21T02:30:01.943763Z" } }, "outputs": [ @@ -1510,7 +1510,7 @@ }, { "cell_type": "markdown", - "id": "b9fefe9f", + "id": "bac19279", "metadata": {}, "source": [ "## SVM with Multiple Classes\n", @@ -1526,13 +1526,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "2fff4fa8", + "id": "267e113d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:31.205816Z", - "iopub.status.busy": "2023-08-07T00:19:31.205709Z", - "iopub.status.idle": "2023-08-07T00:19:31.294925Z", - "shell.execute_reply": "2023-08-07T00:19:31.294593Z" + "iopub.execute_input": "2023-08-21T02:30:01.945725Z", + "iopub.status.busy": "2023-08-21T02:30:01.945611Z", + "iopub.status.idle": "2023-08-21T02:30:02.034378Z", + "shell.execute_reply": "2023-08-21T02:30:02.034069Z" } }, "outputs": [ @@ -1558,7 +1558,7 @@ }, { "cell_type": "markdown", - "id": "b7adc87d", + "id": "a9f4297c", "metadata": {}, "source": [ "We now fit an SVM to the data:" @@ -1567,13 +1567,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "5396f2df", + "id": "64cbebd0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:31.296594Z", - "iopub.status.busy": "2023-08-07T00:19:31.296472Z", - "iopub.status.idle": "2023-08-07T00:19:31.880175Z", - "shell.execute_reply": "2023-08-07T00:19:31.879674Z" + "iopub.execute_input": "2023-08-21T02:30:02.036083Z", + "iopub.status.busy": "2023-08-21T02:30:02.035963Z", + "iopub.status.idle": "2023-08-21T02:30:03.015535Z", + "shell.execute_reply": "2023-08-21T02:30:03.014798Z" }, "lines_to_next_cell": 0 }, @@ -1605,7 +1605,7 @@ }, { "cell_type": "markdown", - "id": "837644f5", + "id": "62c5d16e", "metadata": {}, "source": [ "The `sklearn.svm` library can also be used to perform support vector\n", @@ -1614,7 +1614,7 @@ }, { "cell_type": "markdown", - "id": "a6bc0cbc", + "id": "5c0824b6", "metadata": {}, "source": [ "## Application to Gene Expression Data\n", @@ -1631,13 +1631,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "f63c575e", + "id": "b6e6f12b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:31.882095Z", - "iopub.status.busy": "2023-08-07T00:19:31.881962Z", - "iopub.status.idle": "2023-08-07T00:19:31.959079Z", - "shell.execute_reply": "2023-08-07T00:19:31.958769Z" + "iopub.execute_input": "2023-08-21T02:30:03.017430Z", + "iopub.status.busy": "2023-08-21T02:30:03.017293Z", + "iopub.status.idle": "2023-08-21T02:30:03.099156Z", + "shell.execute_reply": "2023-08-21T02:30:03.098760Z" } }, "outputs": [ @@ -1659,7 +1659,7 @@ }, { "cell_type": "markdown", - "id": "bfd6492c", + "id": "e3fbaa58", "metadata": {}, "source": [ "This data set consists of expression measurements for 2,308\n", @@ -1677,13 +1677,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "32091338", + "id": "273a10b2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:31.960641Z", - "iopub.status.busy": "2023-08-07T00:19:31.960528Z", - "iopub.status.idle": "2023-08-07T00:19:31.990176Z", - "shell.execute_reply": "2023-08-07T00:19:31.989868Z" + "iopub.execute_input": "2023-08-21T02:30:03.101069Z", + "iopub.status.busy": "2023-08-21T02:30:03.100881Z", + "iopub.status.idle": "2023-08-21T02:30:03.130224Z", + "shell.execute_reply": "2023-08-21T02:30:03.129845Z" } }, "outputs": [ @@ -1777,7 +1777,7 @@ }, { "cell_type": "markdown", - "id": "23043ab0", + "id": "31cad43a", "metadata": {}, "source": [ "We see that there are *no* training\n", @@ -1791,13 +1791,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "d9058023", + "id": "bc3079a7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:31.991754Z", - "iopub.status.busy": "2023-08-07T00:19:31.991636Z", - "iopub.status.idle": "2023-08-07T00:19:32.002452Z", - "shell.execute_reply": "2023-08-07T00:19:32.002189Z" + "iopub.execute_input": "2023-08-21T02:30:03.132111Z", + "iopub.status.busy": "2023-08-21T02:30:03.131975Z", + "iopub.status.idle": "2023-08-21T02:30:03.143298Z", + "shell.execute_reply": "2023-08-21T02:30:03.142948Z" } }, "outputs": [ @@ -1889,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "d0d5aba4", + "id": "0d059312", "metadata": {}, "source": [ "We see that using `C=10` yields two test set errors on these data.\n", diff --git a/Ch10-deeplearning-lab.Rmd b/Ch10-deeplearning-lab.Rmd index e6911cc..51674b6 100644 --- a/Ch10-deeplearning-lab.Rmd +++ b/Ch10-deeplearning-lab.Rmd @@ -1,19 +1,3 @@ ---- -jupyter: - jupytext: - cell_metadata_filter: -all - formats: ipynb,Rmd - text_representation: - extension: .Rmd - format_name: rmarkdown - format_version: '1.2' - jupytext_version: 1.14.7 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - # Chapter 10 @@ -872,7 +856,7 @@ for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()): Before we start, we look at some of the training images; similar code produced -Figure 10.5 on page 164. The example below also illustrates +Figure 10.5 on page 447. The example below also illustrates that `TensorDataset` objects can be indexed with integers --- we are choosing random images from the training data by indexing `cifar_train`. In order to display correctly, we must reorder the dimensions by a call to `np.transpose()`. @@ -1705,7 +1689,6 @@ early stopping, since then the test performance would be biased. We form the training dataset similar to our `Hitters` example. - ```{python} datasets = [] diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb index 835512f..2577eac 100644 --- a/Ch10-deeplearning-lab.ipynb +++ b/Ch10-deeplearning-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b672dcf6", + "id": "23016bca", "metadata": {}, "source": [ "\n", @@ -24,7 +24,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "9b5b8319", + "id": "cf431f3f", "metadata": { "lines_to_next_cell": 2 }, @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "c0cb2c87", + "id": "667eff82", "metadata": {}, "source": [ "### Torch-Specific Imports\n", @@ -61,7 +61,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "f66f362c", + "id": "1db00e03", "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "markdown", - "id": "5ce27a8d", + "id": "b3407495", "metadata": {}, "source": [ "There are several other helper packages for `torch`. For instance,\n", @@ -87,7 +87,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "17bce9cb", + "id": "3da0a445", "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "markdown", - "id": "a14990c3", + "id": "e5c55b19", "metadata": {}, "source": [ "The package `pytorch_lightning` is a somewhat higher-level\n", @@ -111,7 +111,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "4c3b6e43", + "id": "bbbf32fe", "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "e5bc78bd", + "id": "cf5ec401", "metadata": {}, "source": [ "In order to reproduce results we use `seed_everything()`. We will also instruct `torch` to use deterministic algorithms\n", @@ -131,7 +131,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6839d8ad", + "id": "3810caf4", "metadata": {}, "outputs": [ { @@ -150,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "1f1490e7", + "id": "c3dea945", "metadata": {}, "source": [ "We will use several datasets shipped with `torchvision` for our\n", @@ -161,7 +161,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "07dba2dd", + "id": "454dc419", "metadata": { "lines_to_next_cell": 0 }, @@ -179,7 +179,7 @@ }, { "cell_type": "markdown", - "id": "36ec305c", + "id": "f7f9578a", "metadata": {}, "source": [ "We have provided a few utilities in `ISLP` specifically for this lab.\n", @@ -197,7 +197,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "89763447", + "id": "cd43a4c6", "metadata": {}, "outputs": [], "source": [ @@ -209,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "dfc13283", + "id": "c5231b9d", "metadata": {}, "source": [ "In addition we have included some helper\n", @@ -226,7 +226,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "ac5ab1b0", + "id": "eaf84e9c", "metadata": {}, "outputs": [], "source": [ @@ -238,7 +238,7 @@ }, { "cell_type": "markdown", - "id": "04f757bd", + "id": "c42bc542", "metadata": {}, "source": [ "Finally, we introduce some utility imports not directly related to\n", @@ -255,7 +255,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "5a5468e6", + "id": "d007a49b", "metadata": { "lines_to_next_cell": 2 }, @@ -267,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "690a5255", + "id": "d0fe1cff", "metadata": {}, "source": [ "## Single Layer Network on Hitters Data\n", @@ -277,7 +277,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "d635398b", + "id": "9da64364", "metadata": { "lines_to_next_cell": 0 }, @@ -289,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "a0fd86f3", + "id": "0e80a8c0", "metadata": {}, "source": [ " We will fit two linear models (least squares and lasso) and compare their performance\n", @@ -305,7 +305,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "2c621749", + "id": "a2cfe999", "metadata": { "lines_to_next_cell": 0 }, @@ -318,7 +318,7 @@ }, { "cell_type": "markdown", - "id": "f32a66d7", + "id": "5f0851bc", "metadata": {}, "source": [ "The `to_numpy()` method above converts `pandas`\n", @@ -331,7 +331,7 @@ }, { "cell_type": "markdown", - "id": "b5565350", + "id": "afe4eb28", "metadata": {}, "source": [ "We now split the data into test and training, fixing the random\n", @@ -341,7 +341,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "cd19596a", + "id": "5c600069", "metadata": {}, "outputs": [], "source": [ @@ -356,7 +356,7 @@ }, { "cell_type": "markdown", - "id": "b82951aa", + "id": "e27a6313", "metadata": {}, "source": [ "### Linear Models\n", @@ -366,7 +366,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "e4bfcd5a", + "id": "6ea4f551", "metadata": {}, "outputs": [ { @@ -388,7 +388,7 @@ }, { "cell_type": "markdown", - "id": "bf3523a8", + "id": "e5fe8c6b", "metadata": {}, "source": [ "Next we fit the lasso using `sklearn`. We are using\n", @@ -402,7 +402,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "36f8290b", + "id": "f1b8b3f5", "metadata": {}, "outputs": [], "source": [ @@ -414,7 +414,7 @@ }, { "cell_type": "markdown", - "id": "9f1bf0ad", + "id": "39a58ed0", "metadata": {}, "source": [ "We need to create a grid of values for $\\lambda$. As is common practice, \n", @@ -425,7 +425,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "5015c394", + "id": "50ce4171", "metadata": { "lines_to_next_cell": 0 }, @@ -440,7 +440,7 @@ }, { "cell_type": "markdown", - "id": "89f6b4e4", + "id": "2cdc3810", "metadata": {}, "source": [ "Note that we had to transform the data first, since the scale of the variables impacts the choice of $\\lambda$.\n", @@ -450,7 +450,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "b5dc7d68", + "id": "94c4ab75", "metadata": {}, "outputs": [], "source": [ @@ -466,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "0b64a882", + "id": "e5262d51", "metadata": {}, "source": [ "We extract the lasso model with best cross-validated mean absolute error, and evaluate its\n", @@ -477,7 +477,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "2c49196f", + "id": "86e45999", "metadata": { "lines_to_next_cell": 0 }, @@ -501,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "61f016c9", + "id": "1f905d89", "metadata": {}, "source": [ "This is similar to the results we got for the linear model fit by least squares. However, these results can vary a lot for different train/test splits; we encourage the reader to try a different seed in code block 12 and rerun the subsequent code up to this point.\n", @@ -519,7 +519,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "df5864e2", + "id": "00ac7606", "metadata": {}, "outputs": [], "source": [ @@ -541,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "4b05cc28", + "id": "a25aab3b", "metadata": {}, "source": [ "The `class` statement identifies the code chunk as a\n", @@ -577,7 +577,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "da601fe1", + "id": "bb7ff7e9", "metadata": {}, "outputs": [], "source": [ @@ -586,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "326f3b54", + "id": "ac0c6bf7", "metadata": {}, "source": [ "The object `self.sequential` is a composition of four maps. The\n", @@ -601,7 +601,7 @@ }, { "cell_type": "markdown", - "id": "a246aedb", + "id": "9e8e69ac", "metadata": {}, "source": [ "The package `torchinfo` provides a `summary()` function that neatly summarizes\n", @@ -612,7 +612,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "15dd23a9", + "id": "b60d34e1", "metadata": { "lines_to_next_cell": 0 }, @@ -658,7 +658,7 @@ }, { "cell_type": "markdown", - "id": "3cb4b8bc", + "id": "aa499e3f", "metadata": {}, "source": [ "We have truncated the end of the output slightly, here and in subsequent uses.\n", @@ -680,7 +680,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "dae83bc5", + "id": "42f63682", "metadata": { "lines_to_next_cell": 0 }, @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "80475fa2", + "id": "6e184dcd", "metadata": {}, "source": [ "We do the same for the test data." @@ -702,7 +702,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "81e217a8", + "id": "57fbf564", "metadata": {}, "outputs": [], "source": [ @@ -713,7 +713,7 @@ }, { "cell_type": "markdown", - "id": "7f49263d", + "id": "09cabaa8", "metadata": {}, "source": [ "Finally, this dataset is passed to a `DataLoader()` which ultimately\n", @@ -737,7 +737,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "e5359e31", + "id": "570bdd73", "metadata": {}, "outputs": [], "source": [ @@ -746,7 +746,7 @@ }, { "cell_type": "markdown", - "id": "105b015f", + "id": "a7bc2151", "metadata": {}, "source": [ "The general training setup in `pytorch_lightning` involves\n", @@ -769,7 +769,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "7a19d6d8", + "id": "c08a4d6d", "metadata": {}, "outputs": [], "source": [ @@ -782,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "8d1f2a76", + "id": "db5447fa", "metadata": {}, "source": [ "Next we must provide a `pytorch_lightning` module that controls\n", @@ -797,7 +797,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "07bc10ef", + "id": "aaa1e593", "metadata": {}, "outputs": [], "source": [ @@ -807,7 +807,7 @@ }, { "cell_type": "markdown", - "id": "1ebf9835", + "id": "8500a2ba", "metadata": {}, "source": [ " By using the `SimpleModule.regression()` method, we indicate that we will use squared-error loss as in\n", @@ -824,7 +824,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "08c71fb4", + "id": "1a4e9b3c", "metadata": {}, "outputs": [], "source": [ @@ -833,7 +833,7 @@ }, { "cell_type": "markdown", - "id": "511617c3", + "id": "77e3c7a5", "metadata": {}, "source": [ "Finally we are ready to train our model and log the results. We\n", @@ -855,7 +855,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "81a8c626", + "id": "2f839fde", "metadata": { "lines_to_next_cell": 0 }, @@ -897,7 +897,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4cb0d8941d43434883b4142c14e198f8", + "model_id": "3f81045e13e641428a7f37ab7ceb43be", "version_major": 2, "version_minor": 0 }, @@ -1569,7 +1569,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "840b55ce195b42edbe9f3fd394dec7e5", + "model_id": "553e586e7cd54ad3bb9e01d0fc37754e", "version_major": 2, "version_minor": 0 }, @@ -1583,7 +1583,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e3d175f2c294fac992ff04733267452", + "model_id": "05c22b9bdd4c48098756a37b57fc963b", "version_major": 2, "version_minor": 0 }, @@ -1597,7 +1597,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2d0477c9d13945bb8a6cbd38620bc93f", + "model_id": "f073bf03d90b4e318352c5de82bb9953", "version_major": 2, "version_minor": 0 }, @@ -1627,7 +1627,7 @@ }, { "cell_type": "markdown", - "id": "abe1a22c", + "id": "4018f616", "metadata": {}, "source": [ "At each step of SGD, the algorithm randomly selects 32 training observations for\n", @@ -1643,7 +1643,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "083670c6", + "id": "672b4410", "metadata": { "lines_to_next_cell": 2 }, @@ -1651,7 +1651,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddc1a0eaac9e4fcda9e91e34abdcc67d", + "model_id": "f0857a2a00a847c084831c51d7fd8dfd", "version_major": 2, "version_minor": 0 }, @@ -1704,7 +1704,7 @@ }, { "cell_type": "markdown", - "id": "c6b3317b", + "id": "0f4e3b11", "metadata": {}, "source": [ "The results of the fit have been logged into a CSV file. We can find the\n", @@ -1720,7 +1720,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "02ba9edf", + "id": "8cf6ef60", "metadata": {}, "outputs": [], "source": [ @@ -1729,7 +1729,7 @@ }, { "cell_type": "markdown", - "id": "daf8ed8d", + "id": "537efe03", "metadata": {}, "source": [ "Since we will produce similar plots in later examples, we write a\n", @@ -1739,7 +1739,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "4184557c", + "id": "67ce1e26", "metadata": { "lines_to_next_cell": 0 }, @@ -1773,7 +1773,7 @@ }, { "cell_type": "markdown", - "id": "026c1c4d", + "id": "53309bb0", "metadata": {}, "source": [ "We now set up our axes, and use our function to produce the MAE plot." @@ -1782,7 +1782,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "3244deeb", + "id": "deb684d2", "metadata": { "lines_to_next_cell": 2 }, @@ -1811,7 +1811,7 @@ }, { "cell_type": "markdown", - "id": "670a3e8f", + "id": "eab05619", "metadata": {}, "source": [ "We can predict directly from the final model, and\n", @@ -1829,7 +1829,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "36bb545e", + "id": "454033dd", "metadata": { "lines_to_next_cell": 0 }, @@ -1853,7 +1853,7 @@ }, { "cell_type": "markdown", - "id": "ae4b9ca6", + "id": "b3625ff5", "metadata": {}, "source": [ " " @@ -1861,7 +1861,7 @@ }, { "cell_type": "markdown", - "id": "b8b7fd3c", + "id": "f46e3883", "metadata": {}, "source": [ "### Cleanup\n", @@ -1875,7 +1875,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "00371f48", + "id": "71b3d0d0", "metadata": { "lines_to_next_cell": 2 }, @@ -1894,7 +1894,7 @@ }, { "cell_type": "markdown", - "id": "5707d61c", + "id": "28d1c832", "metadata": {}, "source": [ "## Multilayer Network on the MNIST Digit Data\n", @@ -1908,7 +1908,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "3e28d6ba", + "id": "def8605c", "metadata": {}, "outputs": [ { @@ -1939,7 +1939,7 @@ }, { "cell_type": "markdown", - "id": "eb8b7e29", + "id": "95ffc346", "metadata": {}, "source": [ "There are 60,000 images in the training data and 10,000 in the test\n", @@ -1963,7 +1963,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "cb04829e", + "id": "8b9e2b8c", "metadata": {}, "outputs": [], "source": [ @@ -1976,7 +1976,7 @@ }, { "cell_type": "markdown", - "id": "1119e22a", + "id": "91256a1b", "metadata": {}, "source": [ "Let’s take a look at the data that will get fed into our network. We loop through the first few\n", @@ -1986,7 +1986,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "c4a265fc", + "id": "a4b95dc6", "metadata": { "lines_to_next_cell": 2 }, @@ -2012,7 +2012,7 @@ }, { "cell_type": "markdown", - "id": "f65ada90", + "id": "12e7eddb", "metadata": {}, "source": [ "We see that the $X$ for each batch consists of 256 images of size `1x28x28`.\n", @@ -2025,7 +2025,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "60339a03", + "id": "17714c25", "metadata": {}, "outputs": [], "source": [ @@ -2051,7 +2051,7 @@ }, { "cell_type": "markdown", - "id": "5b8f87da", + "id": "9893ffb2", "metadata": {}, "source": [ "We see that in the first layer, each `1x28x28` image is flattened, then mapped to\n", @@ -2065,7 +2065,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "1d0f24b2", + "id": "88a4bf46", "metadata": {}, "outputs": [], "source": [ @@ -2074,7 +2074,7 @@ }, { "cell_type": "markdown", - "id": "0ee4771e", + "id": "049febff", "metadata": {}, "source": [ "We can check that the model produces output of expected size based\n", @@ -2084,7 +2084,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "42a4931b", + "id": "ea0d9387", "metadata": {}, "outputs": [ { @@ -2104,7 +2104,7 @@ }, { "cell_type": "markdown", - "id": "628d56f9", + "id": "638026d1", "metadata": {}, "source": [ "Let’s take a look at the summary of the model. Instead of an `input_size` we can pass\n", @@ -2115,7 +2115,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "69e62d88", + "id": "17c34a29", "metadata": {}, "outputs": [ { @@ -2164,7 +2164,7 @@ }, { "cell_type": "markdown", - "id": "f8e77337", + "id": "73e3cd00", "metadata": {}, "source": [ "Having set up both the model and the data module, fitting this model is\n", @@ -2177,7 +2177,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "2027a378", + "id": "a0608bd1", "metadata": {}, "outputs": [], "source": [ @@ -2188,7 +2188,7 @@ }, { "cell_type": "markdown", - "id": "9146b9b5", + "id": "6959c893", "metadata": {}, "source": [ "Now we are ready to go. The final step is to supply training data, and fit the model." @@ -2197,7 +2197,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "4a5e941d", + "id": "cf8e3d9d", "metadata": { "lines_to_next_cell": 0 }, @@ -2239,7 +2239,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "23f1384f37824cc59404e8a856f4962d", + "model_id": "2c7fc7b3fc61455b88cf7020ce62d19e", "version_major": 2, "version_minor": 0 }, @@ -2689,7 +2689,7 @@ }, { "cell_type": "markdown", - "id": "8099bdc9", + "id": "cc8724fa", "metadata": {}, "source": [ "We have suppressed the output here, which is a progress report on the\n", @@ -2707,7 +2707,7 @@ }, { "cell_type": "markdown", - "id": "d6c3bbbc", + "id": "b9dc38ac", "metadata": {}, "source": [ "`SimpleModule.classification()` includes\n", @@ -2720,7 +2720,7 @@ { "cell_type": "code", "execution_count": 43, - "id": "603a278a", + "id": "45e03385", "metadata": { "lines_to_next_cell": 0 }, @@ -2750,7 +2750,7 @@ }, { "cell_type": "markdown", - "id": "654cec05", + "id": "1679f357", "metadata": {}, "source": [ "Once again we evaluate the accuracy using the `test()` method of our trainer. This model achieves\n", @@ -2760,13 +2760,13 @@ { "cell_type": "code", "execution_count": 44, - "id": "93dc968b", + "id": "3a875b78", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3eeaa8a87a31475bb36fe8dd24e694d9", + "model_id": "759de3284ee24f5c8191c1fc922f3e1d", "version_major": 2, "version_minor": 0 }, @@ -2820,7 +2820,7 @@ }, { "cell_type": "markdown", - "id": "5f7dc344", + "id": "12ef4787", "metadata": {}, "source": [ "Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic\n", @@ -2834,7 +2834,7 @@ { "cell_type": "code", "execution_count": 45, - "id": "e1975a3e", + "id": "2f035d83", "metadata": {}, "outputs": [], "source": [ @@ -2855,7 +2855,7 @@ { "cell_type": "code", "execution_count": 46, - "id": "14c79199", + "id": "2cd67ad8", "metadata": { "lines_to_next_cell": 0 }, @@ -2899,7 +2899,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ecb4b2d1cba45a9b762950d7ad365e4", + "model_id": "c932821add0e4296a5ff2122a94b6090", "version_major": 2, "version_minor": 0 }, @@ -3347,7 +3347,7 @@ }, { "cell_type": "markdown", - "id": "f84cfa36", + "id": "20a316a1", "metadata": {}, "source": [ "We fit the model just as before and compute the test results." @@ -3356,7 +3356,7 @@ { "cell_type": "code", "execution_count": 47, - "id": "acb6f88d", + "id": "3900d16e", "metadata": { "lines_to_next_cell": 0 }, @@ -3364,7 +3364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96180813745947aa8b3705fa737d63a0", + "model_id": "63d666ec843544bf804c3fc6fc5fd6ab", "version_major": 2, "version_minor": 0 }, @@ -3418,7 +3418,7 @@ }, { "cell_type": "markdown", - "id": "05244a5e", + "id": "ca80aa75", "metadata": {}, "source": [ "The accuracy is above 90% even for this pretty simple model.\n", @@ -3430,7 +3430,7 @@ { "cell_type": "code", "execution_count": 48, - "id": "f5b3f811", + "id": "679f2ea5", "metadata": { "lines_to_next_cell": 2 }, @@ -3450,7 +3450,7 @@ }, { "cell_type": "markdown", - "id": "b5b395bb", + "id": "dd8ba586", "metadata": {}, "source": [ "## Convolutional Neural Networks\n", @@ -3461,7 +3461,7 @@ { "cell_type": "code", "execution_count": 49, - "id": "e1caa7ac", + "id": "e4af6604", "metadata": {}, "outputs": [ { @@ -3484,7 +3484,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "9e94a7b4", + "id": "2b613ecc", "metadata": {}, "outputs": [], "source": [ @@ -3501,7 +3501,7 @@ }, { "cell_type": "markdown", - "id": "219ccc6f", + "id": "af1d3cdc", "metadata": {}, "source": [ "The `CIFAR100` dataset consists of 50,000 training images, each represented by a three-dimensional tensor:\n", @@ -3515,7 +3515,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "32c78c06", + "id": "4b325cb4", "metadata": { "lines_to_next_cell": 0 }, @@ -3530,7 +3530,7 @@ }, { "cell_type": "markdown", - "id": "e4570164", + "id": "f361276e", "metadata": {}, "source": [ "We again look at the shape of typical batches in our data loaders." @@ -3539,7 +3539,7 @@ { "cell_type": "code", "execution_count": 52, - "id": "b3c27322", + "id": "cb3d00cb", "metadata": { "lines_to_next_cell": 2 }, @@ -3565,11 +3565,11 @@ }, { "cell_type": "markdown", - "id": "f6152280", + "id": "08771862", "metadata": {}, "source": [ "Before we start, we look at some of the training images; similar code produced\n", - "Figure 10.5 on page 164. The example below also illustrates\n", + "Figure 10.5 on page 447. The example below also illustrates\n", "that `TensorDataset` objects can be indexed with integers --- we are choosing\n", "random images from the training data by indexing `cifar_train`. In order to display correctly,\n", "we must reorder the dimensions by a call to `np.transpose()`." @@ -3578,7 +3578,7 @@ { "cell_type": "code", "execution_count": 53, - "id": "c626e0ff", + "id": "60d09656", "metadata": { "lines_to_next_cell": 0 }, @@ -3611,7 +3611,7 @@ }, { "cell_type": "markdown", - "id": "2a1c4832", + "id": "642140af", "metadata": {}, "source": [ "Here the `imshow()` method recognizes from the shape of its argument that it is a 3-dimensional array, with the last dimension indexing the three RGB color channels.\n", @@ -3628,7 +3628,7 @@ { "cell_type": "code", "execution_count": 54, - "id": "9d5bcdf3", + "id": "f823da11", "metadata": {}, "outputs": [], "source": [ @@ -3652,7 +3652,7 @@ }, { "cell_type": "markdown", - "id": "a7204121", + "id": "00927159", "metadata": {}, "source": [ "Notice that we used the `padding = \"same\"` argument to\n", @@ -3673,7 +3673,7 @@ { "cell_type": "code", "execution_count": 55, - "id": "3e13e9bc", + "id": "1a172f7e", "metadata": {}, "outputs": [], "source": [ @@ -3700,7 +3700,7 @@ }, { "cell_type": "markdown", - "id": "1b07fb1a", + "id": "8455079f", "metadata": {}, "source": [ "We build the model and look at the summary. (We had created examples of `X_` earlier.)" @@ -3709,7 +3709,7 @@ { "cell_type": "code", "execution_count": 56, - "id": "15c4a382", + "id": "651e62b4", "metadata": { "lines_to_next_cell": 2 }, @@ -3772,7 +3772,7 @@ }, { "cell_type": "markdown", - "id": "b168c198", + "id": "7dd67ce7", "metadata": {}, "source": [ "The total number of trainable parameters is 964,516.\n", @@ -3806,7 +3806,7 @@ { "cell_type": "code", "execution_count": 57, - "id": "4a40238a", + "id": "63f2650e", "metadata": {}, "outputs": [], "source": [ @@ -3820,7 +3820,7 @@ { "cell_type": "code", "execution_count": 58, - "id": "8aab2c62", + "id": "a3e4bc28", "metadata": {}, "outputs": [ { @@ -3860,7 +3860,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8473f6db1fdc40b7909b66fc6410277d", + "model_id": "40c811fa26da4690a95838e6ab0a8a98", "version_major": 2, "version_minor": 0 }, @@ -4310,7 +4310,7 @@ }, { "cell_type": "markdown", - "id": "090f6400", + "id": "c3fc9402", "metadata": {}, "source": [ "This model can take 10 minutes or more to run and achieves about 42% accuracy on the test\n", @@ -4326,7 +4326,7 @@ { "cell_type": "code", "execution_count": 59, - "id": "12474ef6", + "id": "6b161d93", "metadata": { "lines_to_next_cell": 0 }, @@ -4357,7 +4357,7 @@ }, { "cell_type": "markdown", - "id": "f3fe0cd4", + "id": "71dea0c8", "metadata": {}, "source": [ "Finally, we evaluate our model on our test data." @@ -4366,7 +4366,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "9d632437", + "id": "ab8a91dc", "metadata": { "lines_to_next_cell": 2 }, @@ -4374,7 +4374,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc46f12820ff4ca281d8a035a53fa610", + "model_id": "6dced9ab160c4c30b094a877f330efba", "version_major": 2, "version_minor": 0 }, @@ -4428,7 +4428,7 @@ }, { "cell_type": "markdown", - "id": "4b69e259", + "id": "11d0e8b3", "metadata": {}, "source": [ "### Hardware Acceleration\n", @@ -4449,7 +4449,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "52a43158", + "id": "6d9962ae", "metadata": { "lines_to_next_cell": 0 }, @@ -4491,7 +4491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "efb32ed16b3946ebbdb7c6a1714c7347", + "model_id": "6409051044c94ad9af785a42df78d36e", "version_major": 2, "version_minor": 0 }, @@ -4940,7 +4940,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae6af1d96e5a4d1fa048bbbf0a3f5b7b", + "model_id": "4f37d3b614314b6fbf9dfb3df1775948", "version_major": 2, "version_minor": 0 }, @@ -4969,7 +4969,7 @@ }, { "cell_type": "markdown", - "id": "b72de871", + "id": "3faab186", "metadata": {}, "source": [ "This yields approximately two- or three-fold acceleration for each epoch.\n", @@ -4979,7 +4979,7 @@ }, { "cell_type": "markdown", - "id": "b86d6ef4", + "id": "31759785", "metadata": {}, "source": [ "## Using Pretrained CNN Models\n", @@ -5001,7 +5001,7 @@ { "cell_type": "code", "execution_count": 62, - "id": "db2febe9", + "id": "a71c9acb", "metadata": { "lines_to_next_cell": 2 }, @@ -5031,7 +5031,7 @@ }, { "cell_type": "markdown", - "id": "f6696ee2", + "id": "89071e87", "metadata": {}, "source": [ "We now set up the trained network with the weights we read in code block~6. The model has 50 layers, with a fair bit of complexity." @@ -5040,7 +5040,7 @@ { "cell_type": "code", "execution_count": 63, - "id": "2aabd724", + "id": "4f890244", "metadata": { "lines_to_next_cell": 0 }, @@ -5255,7 +5255,7 @@ }, { "cell_type": "markdown", - "id": "c4ee6ebe", + "id": "185bb4b5", "metadata": {}, "source": [ "We set the mode to `eval()` to ensure that the model is ready to predict on new data." @@ -5264,7 +5264,7 @@ { "cell_type": "code", "execution_count": 64, - "id": "6d27342d", + "id": "c4be9922", "metadata": { "lines_to_next_cell": 0 }, @@ -5461,7 +5461,7 @@ }, { "cell_type": "markdown", - "id": "3c1fbc71", + "id": "f0a3c519", "metadata": {}, "source": [ "Inspecting the output above, we see that when setting up the\n", @@ -5474,7 +5474,7 @@ { "cell_type": "code", "execution_count": 65, - "id": "efdbeda1", + "id": "2dc63d85", "metadata": {}, "outputs": [], "source": [ @@ -5483,7 +5483,7 @@ }, { "cell_type": "markdown", - "id": "38620865", + "id": "15ec1321", "metadata": {}, "source": [ "Let’s look at the predicted probabilities for each of the top 3 choices. First we compute\n", @@ -5495,7 +5495,7 @@ { "cell_type": "code", "execution_count": 66, - "id": "82ceab1c", + "id": "711d5ba7", "metadata": {}, "outputs": [], "source": [ @@ -5505,7 +5505,7 @@ }, { "cell_type": "markdown", - "id": "0e3ae755", + "id": "3b514c1e", "metadata": {}, "source": [ "In order to see the class labels, we must download the index file associated with `imagenet`. {This is avalable from the book website and [s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json).}" @@ -5514,7 +5514,7 @@ { "cell_type": "code", "execution_count": 67, - "id": "921ee168", + "id": "b22f70d8", "metadata": {}, "outputs": [], "source": [ @@ -5528,7 +5528,7 @@ }, { "cell_type": "markdown", - "id": "9fdd7587", + "id": "a5812782", "metadata": {}, "source": [ "We’ll now construct a data frame for each image file\n", @@ -5539,7 +5539,7 @@ { "cell_type": "code", "execution_count": 68, - "id": "e7770017", + "id": "b19c6bd1", "metadata": { "lines_to_next_cell": 2 }, @@ -5592,7 +5592,7 @@ }, { "cell_type": "markdown", - "id": "12862208", + "id": "cd6bd40b", "metadata": {}, "source": [ "We see that the model\n", @@ -5605,7 +5605,7 @@ { "cell_type": "code", "execution_count": 69, - "id": "af2f9856", + "id": "ba80b615", "metadata": { "lines_to_next_cell": 2 }, @@ -5622,7 +5622,7 @@ }, { "cell_type": "markdown", - "id": "d9e8d59c", + "id": "2e6eafaf", "metadata": {}, "source": [ "## IMDB Document Classification\n", @@ -5649,7 +5649,7 @@ { "cell_type": "code", "execution_count": 70, - "id": "afd98123", + "id": "ba6d2d2c", "metadata": { "lines_to_next_cell": 0 }, @@ -5676,7 +5676,7 @@ }, { "cell_type": "markdown", - "id": "28a01855", + "id": "ebeeb069", "metadata": {}, "source": [ "The datasets `imdb_seq_train` and `imdb_seq_test` are\n", @@ -5694,7 +5694,7 @@ { "cell_type": "code", "execution_count": 71, - "id": "5981eb05", + "id": "93bda908", "metadata": {}, "outputs": [ { @@ -5715,7 +5715,7 @@ }, { "cell_type": "markdown", - "id": "b579cb29", + "id": "6de86e76", "metadata": {}, "source": [ "For our first model, we have created a binary feature for each\n", @@ -5728,7 +5728,7 @@ { "cell_type": "code", "execution_count": 72, - "id": "f08024ba", + "id": "40943b7d", "metadata": { "lines_to_next_cell": 0 }, @@ -5746,7 +5746,7 @@ }, { "cell_type": "markdown", - "id": "c070a319", + "id": "9328d75c", "metadata": {}, "source": [ "We’ll use a two-layer model for our first model." @@ -5755,7 +5755,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "7fd34906", + "id": "2117fd9f", "metadata": { "lines_to_next_cell": 0 }, @@ -5783,7 +5783,7 @@ }, { "cell_type": "markdown", - "id": "fcc2a8af", + "id": "845b4d8e", "metadata": {}, "source": [ "We now instantiate our model and look at a summary." @@ -5792,7 +5792,7 @@ { "cell_type": "code", "execution_count": 74, - "id": "56f74fdb", + "id": "66d0b710", "metadata": {}, "outputs": [ { @@ -5836,7 +5836,7 @@ }, { "cell_type": "markdown", - "id": "a32aca43", + "id": "c8bdad40", "metadata": {}, "source": [ "We’ll again use\n", @@ -5854,7 +5854,7 @@ { "cell_type": "code", "execution_count": 75, - "id": "3da7e0bc", + "id": "9df8b4cf", "metadata": {}, "outputs": [], "source": [ @@ -5866,7 +5866,7 @@ }, { "cell_type": "markdown", - "id": "940c8342", + "id": "95cc3b4f", "metadata": {}, "source": [ "Having loaded the datasets into a data module\n", @@ -5877,7 +5877,7 @@ { "cell_type": "code", "execution_count": 76, - "id": "3b6de185", + "id": "73684c66", "metadata": {}, "outputs": [ { @@ -5925,7 +5925,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57e25e1e102745c18849507d8fe93fe3", + "model_id": "253cf3e077d845569cdc459ef74902b6", "version_major": 2, "version_minor": 0 }, @@ -6376,7 +6376,7 @@ }, { "cell_type": "markdown", - "id": "5985c44a", + "id": "f2e03c88", "metadata": {}, "source": [ "Evaluating the test error yields roughly 86% accuracy." @@ -6385,7 +6385,7 @@ { "cell_type": "code", "execution_count": 77, - "id": "97f86a32", + "id": "01c6e5ff", "metadata": { "lines_to_next_cell": 2 }, @@ -6393,7 +6393,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57a8b619aaf049b4b06550a47d5e3611", + "model_id": "f2b40a42021f4a9183c3d7d2d545e83b", "version_major": 2, "version_minor": 0 }, @@ -6447,7 +6447,7 @@ }, { "cell_type": "markdown", - "id": "3d677b24", + "id": "c1976042", "metadata": {}, "source": [ "### Comparison to Lasso\n", @@ -6460,7 +6460,7 @@ { "cell_type": "code", "execution_count": 78, - "id": "e36e1542", + "id": "4d5b9d1d", "metadata": {}, "outputs": [], "source": [ @@ -6473,7 +6473,7 @@ }, { "cell_type": "markdown", - "id": "8216f2e7", + "id": "b78eb64e", "metadata": {}, "source": [ "Similar to what we did in\n", @@ -6484,7 +6484,7 @@ { "cell_type": "code", "execution_count": 79, - "id": "ee6d6859", + "id": "e2a88e57", "metadata": { "lines_to_next_cell": 0 }, @@ -6497,7 +6497,7 @@ }, { "cell_type": "markdown", - "id": "cf9b703f", + "id": "81380bee", "metadata": {}, "source": [ "With `LogisticRegression()` the regularization parameter\n", @@ -6509,7 +6509,7 @@ { "cell_type": "code", "execution_count": 80, - "id": "35a6e3c2", + "id": "9a3cf7a3", "metadata": { "lines_to_next_cell": 0 }, @@ -6524,7 +6524,7 @@ }, { "cell_type": "markdown", - "id": "49cec308", + "id": "9ee15d44", "metadata": {}, "source": [ "The path of 50 values takes approximately 40 seconds to run." @@ -6533,7 +6533,7 @@ { "cell_type": "code", "execution_count": 81, - "id": "a8943a6a", + "id": "b46f02c2", "metadata": {}, "outputs": [], "source": [ @@ -6549,7 +6549,7 @@ }, { "cell_type": "markdown", - "id": "4f20738d", + "id": "eb3e3871", "metadata": {}, "source": [ "The coefficient and intercepts have an extraneous dimension which can be removed\n", @@ -6559,7 +6559,7 @@ { "cell_type": "code", "execution_count": 82, - "id": "08268ae5", + "id": "e5fb6afa", "metadata": { "lines_to_next_cell": 0 }, @@ -6571,7 +6571,7 @@ }, { "cell_type": "markdown", - "id": "94991ca8", + "id": "f66fa37c", "metadata": {}, "source": [ "We’ll now make a plot to compare our neural network results with the\n", @@ -6581,7 +6581,7 @@ { "cell_type": "code", "execution_count": 83, - "id": "e98f4506", + "id": "cad28f1a", "metadata": { "lines_to_next_cell": 0 }, @@ -6613,7 +6613,7 @@ }, { "cell_type": "markdown", - "id": "aac5c38c", + "id": "c58ff7cb", "metadata": {}, "source": [ "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure. This is useful\n", @@ -6624,7 +6624,7 @@ { "cell_type": "code", "execution_count": 84, - "id": "6925d53a", + "id": "a66ecdd8", "metadata": { "lines_to_next_cell": 0 }, @@ -6660,7 +6660,7 @@ }, { "cell_type": "markdown", - "id": "2a8b7736", + "id": "fa1e2550", "metadata": {}, "source": [ "From the graphs we see that the accuracy of the lasso logistic regression peaks at about $0.88$, as it does for the neural network.\n", @@ -6671,7 +6671,7 @@ { "cell_type": "code", "execution_count": 85, - "id": "6d1f7885", + "id": "62440c1c", "metadata": { "lines_to_next_cell": 2 }, @@ -6687,7 +6687,7 @@ }, { "cell_type": "markdown", - "id": "4f79c23a", + "id": "fe67520f", "metadata": {}, "source": [ "## Recurrent Neural Networks\n", @@ -6697,7 +6697,7 @@ }, { "cell_type": "markdown", - "id": "3d376496", + "id": "a9bcdd2a", "metadata": {}, "source": [ "### Sequential Models for Document Classification\n", @@ -6717,7 +6717,7 @@ { "cell_type": "code", "execution_count": 86, - "id": "b5c2b72c", + "id": "c73d6e28", "metadata": {}, "outputs": [], "source": [ @@ -6731,7 +6731,7 @@ }, { "cell_type": "markdown", - "id": "4d1b3ef1", + "id": "1edd3143", "metadata": {}, "source": [ "The first layer of the RNN is an embedding layer of size 32, which will be\n", @@ -6748,7 +6748,7 @@ }, { "cell_type": "markdown", - "id": "fe55c7fa", + "id": "b73e08ab", "metadata": {}, "source": [ "The second layer is an LSTM with 32 units, and the output\n", @@ -6760,7 +6760,7 @@ { "cell_type": "code", "execution_count": 87, - "id": "8985cdb1", + "id": "cc9bbd00", "metadata": { "lines_to_next_cell": 0 }, @@ -6781,7 +6781,7 @@ }, { "cell_type": "markdown", - "id": "78fc988d", + "id": "5e9f28dd", "metadata": {}, "source": [ "We instantiate and take a look at the summary of the model, using the\n", @@ -6791,7 +6791,7 @@ { "cell_type": "code", "execution_count": 88, - "id": "79184187", + "id": "5c9ffb46", "metadata": {}, "outputs": [ { @@ -6833,7 +6833,7 @@ }, { "cell_type": "markdown", - "id": "d5b2c85e", + "id": "5087ff10", "metadata": {}, "source": [ "The 10,003 is suppressed in the summary, but we see it in the\n", @@ -6843,7 +6843,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "29036a47", + "id": "a2d6ddfd", "metadata": {}, "outputs": [], "source": [ @@ -6854,7 +6854,7 @@ { "cell_type": "code", "execution_count": 90, - "id": "0a31576a", + "id": "1d76f970", "metadata": { "lines_to_next_cell": 0 }, @@ -6896,7 +6896,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4abe857cc251478b8855f53164fa5b45", + "model_id": "e24a4171456b412db47d6577cb336c37", "version_major": 2, "version_minor": 0 }, @@ -7206,7 +7206,7 @@ }, { "cell_type": "markdown", - "id": "a20a2113", + "id": "1cbf1b42", "metadata": {}, "source": [ "The rest is now similar to other networks we have fit. We\n", @@ -7216,13 +7216,13 @@ { "cell_type": "code", "execution_count": 91, - "id": "b2602a79", + "id": "d8a60d35", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1648bfd17bc04050b412c36afb32e092", + "model_id": "4e512ef8a7d348eea7f423a9ed96c86e", "version_major": 2, "version_minor": 0 }, @@ -7240,8 +7240,8 @@ "┃ Runningstage.testing ┃ ┃\n", "┃ metric ┃ DataLoader 0 ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ test_accuracy │ 0.8388800024986267 │\n", - "│ test_loss │ 0.8145671486854553 │\n", + "│ test_accuracy │ 0.8480799794197083 │\n", + "│ test_loss │ 0.7677657604217529 │\n", "└───────────────────────────┴───────────────────────────┘\n", "\n" ], @@ -7250,8 +7250,8 @@ "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.testing \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m┃\n", "┃\u001b[1m \u001b[0m\u001b[1m metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│\u001b[36m \u001b[0m\u001b[36m test_accuracy \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.8388800024986267 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test_loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.8145671486854553 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test_accuracy \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.8480799794197083 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36m test_loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.7677657604217529 \u001b[0m\u001b[35m \u001b[0m│\n", "└───────────────────────────┴───────────────────────────┘\n" ] }, @@ -7261,7 +7261,7 @@ { "data": { "text/plain": [ - "[{'test_loss': 0.8145671486854553, 'test_accuracy': 0.8388800024986267}]" + "[{'test_loss': 0.7677657604217529, 'test_accuracy': 0.8480799794197083}]" ] }, "execution_count": 91, @@ -7275,7 +7275,7 @@ }, { "cell_type": "markdown", - "id": "54857d49", + "id": "deca21cd", "metadata": {}, "source": [ "We once again show the learning progress, followed by cleanup." @@ -7284,7 +7284,7 @@ { "cell_type": "code", "execution_count": 92, - "id": "32c3e3da", + "id": "65d7276c", "metadata": { "lines_to_next_cell": 2 }, @@ -7301,7 +7301,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -7325,7 +7325,7 @@ { "cell_type": "code", "execution_count": 93, - "id": "be5a979e", + "id": "c6f2d6c4", "metadata": { "lines_to_next_cell": 2 }, @@ -7341,7 +7341,7 @@ }, { "cell_type": "markdown", - "id": "240618c9", + "id": "f920c659", "metadata": {}, "source": [ "### Time Series Prediction\n", @@ -7353,7 +7353,7 @@ { "cell_type": "code", "execution_count": 94, - "id": "cf219016", + "id": "f3e17682", "metadata": {}, "outputs": [], "source": [ @@ -7368,7 +7368,7 @@ }, { "cell_type": "markdown", - "id": "2ebf00d3", + "id": "e621c2eb", "metadata": {}, "source": [ "Next we set up the lagged versions of the data, dropping\n", @@ -7378,7 +7378,7 @@ { "cell_type": "code", "execution_count": 95, - "id": "c0d6bb22", + "id": "78707eda", "metadata": {}, "outputs": [], "source": [ @@ -7393,7 +7393,7 @@ }, { "cell_type": "markdown", - "id": "53d15ddf", + "id": "bb83e654", "metadata": {}, "source": [ "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n", @@ -7403,7 +7403,7 @@ { "cell_type": "code", "execution_count": 96, - "id": "9de511d5", + "id": "4d894824", "metadata": { "lines_to_next_cell": 2 }, @@ -7431,7 +7431,7 @@ }, { "cell_type": "markdown", - "id": "9c61eaa2", + "id": "f1889769", "metadata": {}, "source": [ "We first fit a simple linear model and compute the $R^2$ on the test data using\n", @@ -7441,7 +7441,7 @@ { "cell_type": "code", "execution_count": 97, - "id": "89666a56", + "id": "4d7f5ce0", "metadata": {}, "outputs": [ { @@ -7463,7 +7463,7 @@ }, { "cell_type": "markdown", - "id": "c3997039", + "id": "02c7294f", "metadata": {}, "source": [ "We refit this model, including the factor variable `day_of_week`.\n", @@ -7474,7 +7474,7 @@ { "cell_type": "code", "execution_count": 98, - "id": "23ebf124", + "id": "a6b371bb", "metadata": { "lines_to_next_cell": 0 }, @@ -7487,7 +7487,7 @@ }, { "cell_type": "markdown", - "id": "8df4e475", + "id": "92fda2e1", "metadata": {}, "source": [ " Note that we do not have\n", @@ -7498,7 +7498,7 @@ { "cell_type": "code", "execution_count": 99, - "id": "4190a4dc", + "id": "a2a8cc85", "metadata": { "lines_to_next_cell": 0 }, @@ -7521,7 +7521,7 @@ }, { "cell_type": "markdown", - "id": "d98cc235", + "id": "68a22c64", "metadata": {}, "source": [ "This model achieves an $R^2$ of about 46%." @@ -7529,7 +7529,7 @@ }, { "cell_type": "markdown", - "id": "131bfc1c", + "id": "1cca42e4", "metadata": {}, "source": [ "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n", @@ -7550,7 +7550,7 @@ { "cell_type": "code", "execution_count": 100, - "id": "a2b41f92", + "id": "8ee6e6a3", "metadata": { "lines_to_next_cell": 0 }, @@ -7581,7 +7581,7 @@ }, { "cell_type": "markdown", - "id": "7b301214", + "id": "a1f41334", "metadata": {}, "source": [ "We now reshape the data." @@ -7590,7 +7590,7 @@ { "cell_type": "code", "execution_count": 101, - "id": "dde73c9e", + "id": "d35ceb54", "metadata": { "lines_to_next_cell": 0 }, @@ -7613,7 +7613,7 @@ }, { "cell_type": "markdown", - "id": "4fa6cd7a", + "id": "11137e1d", "metadata": {}, "source": [ "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n", @@ -7628,7 +7628,7 @@ { "cell_type": "code", "execution_count": 102, - "id": "6f9d6357", + "id": "9e3dc6d5", "metadata": {}, "outputs": [], "source": [ @@ -7649,7 +7649,7 @@ }, { "cell_type": "markdown", - "id": "a5d720e7", + "id": "1fd566f7", "metadata": {}, "source": [ "We fit the model in a similar fashion to previous networks. We\n", @@ -7659,14 +7659,13 @@ "early stopping, since then the test performance would be biased.\n", "\n", "We form the training dataset similar to\n", - "our `Hitters` example.\n", - " " + "our `Hitters` example." ] }, { "cell_type": "code", "execution_count": 103, - "id": "a21ca47e", + "id": "df5e5ab6", "metadata": {}, "outputs": [], "source": [ @@ -7680,7 +7679,7 @@ }, { "cell_type": "markdown", - "id": "0d627892", + "id": "16565d83", "metadata": {}, "source": [ "Following our usual pattern, we inspect the summary." @@ -7689,7 +7688,7 @@ { "cell_type": "code", "execution_count": 104, - "id": "8fa26b87", + "id": "d7f49bec", "metadata": { "lines_to_next_cell": 0 }, @@ -7732,7 +7731,7 @@ }, { "cell_type": "markdown", - "id": "065db586", + "id": "ad57780a", "metadata": {}, "source": [ "We again put the two datasets into a data module, with a\n", @@ -7742,7 +7741,7 @@ { "cell_type": "code", "execution_count": 105, - "id": "9b871361", + "id": "ea7ce0f4", "metadata": { "lines_to_next_cell": 0 }, @@ -7757,7 +7756,7 @@ }, { "cell_type": "markdown", - "id": "8e6d61d8", + "id": "d0d60c8b", "metadata": {}, "source": [ "We run some data through our model to be sure the sizes match up correctly." @@ -7766,7 +7765,7 @@ { "cell_type": "code", "execution_count": 106, - "id": "b63d1f85", + "id": "ccd77738", "metadata": {}, "outputs": [ { @@ -7789,7 +7788,7 @@ }, { "cell_type": "markdown", - "id": "7fc69ade", + "id": "c9397b7f", "metadata": {}, "source": [ "We follow our previous example for setting up a trainer for a\n", @@ -7800,7 +7799,7 @@ { "cell_type": "code", "execution_count": 107, - "id": "c5f32a4f", + "id": "96e04e3f", "metadata": {}, "outputs": [], "source": [ @@ -7813,7 +7812,7 @@ }, { "cell_type": "markdown", - "id": "ed00be6a", + "id": "9d7ef6fc", "metadata": {}, "source": [ "Fitting the model should by now be familiar.\n", @@ -7823,7 +7822,7 @@ { "cell_type": "code", "execution_count": 108, - "id": "6bd98eb9", + "id": "fc6ba2ca", "metadata": { "lines_to_next_cell": 2 }, @@ -7865,7 +7864,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f50cb3862a6e4ddcbcb7131a9c3e6c07", + "model_id": "cd881313814b4b319883de36a456b21e", "version_major": 2, "version_minor": 0 }, @@ -8537,7 +8536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e613a523fb24350a02e4af53fc31c0b", + "model_id": "c0851768dd544162913f87e0e0cb118e", "version_major": 2, "version_minor": 0 }, @@ -8551,7 +8550,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c4db1eab382549f1b1beddfad3368138", + "model_id": "6cb1dc51cbad4e1f829a074e29d3a47e", "version_major": 2, "version_minor": 0 }, @@ -8565,7 +8564,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe986a9354124fab8835d0b2d93529dd", + "model_id": "608984e04fc847d7860dfb48bbcdda9f", "version_major": 2, "version_minor": 0 }, @@ -8579,7 +8578,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8cb7c8dcfc8407e9bcb453d75fec4fb", + "model_id": "bef4bc23212c41c497e518473db55672", "version_major": 2, "version_minor": 0 }, @@ -8593,7 +8592,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3bb249322a440fea649e953c6f8e6f9", + "model_id": "890d82c78d59444ea97f6000b0d17fec", "version_major": 2, "version_minor": 0 }, @@ -8607,7 +8606,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fa670100f02b47cf9ec4c3e6bad634e7", + "model_id": "84f285e1262e448186175b5f6ea8a100", "version_major": 2, "version_minor": 0 }, @@ -8621,7 +8620,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a89941f764245c2ada50a90a13c2de5", + "model_id": "6bcddf29e018487ab3cbd65b9360a31c", "version_major": 2, "version_minor": 0 }, @@ -8635,7 +8634,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1620d945db8415eb67ca9c7c9204437", + "model_id": "0f5f0bf0e3094d359222a40cfb883e67", "version_major": 2, "version_minor": 0 }, @@ -8649,7 +8648,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ccaab29c24594688aab4e276f0602911", + "model_id": "436f0a6e3a7a4457b6b54fb1c19a9fc1", "version_major": 2, "version_minor": 0 }, @@ -8663,7 +8662,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "985770bbcdc54a978e25689ec98b9ec8", + "model_id": "153517df5e244608a4df90e6d5b3f2fb", "version_major": 2, "version_minor": 0 }, @@ -8677,7 +8676,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "649a7264f6e84529ab9292bab0b77432", + "model_id": "966bf641ff264d9ba465a989337d24c8", "version_major": 2, "version_minor": 0 }, @@ -8691,7 +8690,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bbd262d3471646e38208a319c863cb75", + "model_id": "232ec0a0183e462f92422b1991162466", "version_major": 2, "version_minor": 0 }, @@ -8705,7 +8704,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58ad0974b15546609feec97cc94b6a7a", + "model_id": "94eebdb823b9416abdb1454fa8b713a1", "version_major": 2, "version_minor": 0 }, @@ -8719,7 +8718,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d6114f44e51d4586b911578aa52ed8e5", + "model_id": "63e68c7095d74639a33c6b422dde8e6b", "version_major": 2, "version_minor": 0 }, @@ -8733,7 +8732,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d42389c558874de3932eb5bc0c4165cb", + "model_id": "858b9ede991d4db5a65f3738145661b0", "version_major": 2, "version_minor": 0 }, @@ -8747,7 +8746,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "892652c223b4426b8e809a0cc36985a2", + "model_id": "1320a563fa1141f48ec1121a4da566dd", "version_major": 2, "version_minor": 0 }, @@ -8761,7 +8760,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc6a9c7712ac47e7be436a5e53c1d203", + "model_id": "c258eee32312493f820b0be1001b702d", "version_major": 2, "version_minor": 0 }, @@ -8775,7 +8774,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "71474f43feee42a78f5b2dd1441ebea8", + "model_id": "cfe59e5c336b483595b2c2f3e5002fe5", "version_major": 2, "version_minor": 0 }, @@ -8789,7 +8788,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d35c015e070a4066a00638dfea294e20", + "model_id": "da49b9baca8d43deb73852ac22411b59", "version_major": 2, "version_minor": 0 }, @@ -8803,7 +8802,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bbe8d6a738a2452eb96907cfd94a865e", + "model_id": "2edefd368841420799756fdeb9eb350e", "version_major": 2, "version_minor": 0 }, @@ -8817,7 +8816,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c4e37d0e56cf4433bae37ba25dd6addc", + "model_id": "fb1ab9dd650c4327b9e08083ea6f855e", "version_major": 2, "version_minor": 0 }, @@ -8831,7 +8830,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "508e2b4b875b4f3eb89460503b82a44b", + "model_id": "337548c8b3a649b3bbb2aa022d0bc33c", "version_major": 2, "version_minor": 0 }, @@ -8845,7 +8844,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe84b5d4fff64e4e84eb3baf63f49ebb", + "model_id": "469f0f8eaf84468689dc81a6866590c6", "version_major": 2, "version_minor": 0 }, @@ -8859,7 +8858,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "234a8305cd0e4671bafd6347a508006a", + "model_id": "5a3d2d6c73104569b2431592878a0680", "version_major": 2, "version_minor": 0 }, @@ -8873,7 +8872,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9314bb524e4c4c988e0637f0b426dea3", + "model_id": "0a4f360d5a1c4e688c806475aa15e2aa", "version_major": 2, "version_minor": 0 }, @@ -8887,7 +8886,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73bb408fb3b14067bc5a00c51f2b46ab", + "model_id": "32861129effb47cdb392fa0a20b636c3", "version_major": 2, "version_minor": 0 }, @@ -8901,7 +8900,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80e55275d2694434ba664645a53cfdfb", + "model_id": "0713f563c1fc4d6292be6340e83f6417", "version_major": 2, "version_minor": 0 }, @@ -8915,7 +8914,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af57619b39e141b0a849207567f12685", + "model_id": "1364d3ae21274234a26921be5a610c57", "version_major": 2, "version_minor": 0 }, @@ -8929,7 +8928,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e9d1664e7094f3a80c4c29a64c69850", + "model_id": "c5e4630ea30945fd862bce8a68c9eaff", "version_major": 2, "version_minor": 0 }, @@ -8943,7 +8942,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cbadff921ed84c079d2920fa8f108d79", + "model_id": "fb014ea654fa49c6b29fda1c42a4239b", "version_major": 2, "version_minor": 0 }, @@ -8957,7 +8956,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a4f763710f54a64a164ed4e8ade0cd4", + "model_id": "fc9065e42c5946f88aa84da055bf968e", "version_major": 2, "version_minor": 0 }, @@ -8971,7 +8970,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7ae34405e0904941bde7ac34156ef5d9", + "model_id": "b33c56819c07474596c522b66e69d8e0", "version_major": 2, "version_minor": 0 }, @@ -8985,7 +8984,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "758b295e2ebc4f818bf7634dc9e6511b", + "model_id": "e227733b97fd411fa91be941aa0826f3", "version_major": 2, "version_minor": 0 }, @@ -8999,7 +8998,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "70ed4e60b2724c27bba5e161f5d6c0e9", + "model_id": "b0de6788585a43d387ee192d3e919582", "version_major": 2, "version_minor": 0 }, @@ -9013,7 +9012,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1838ceec52264c5b9ef4bbf089932af4", + "model_id": "95f701a89b174f96a7121b1741fcb9fe", "version_major": 2, "version_minor": 0 }, @@ -9027,7 +9026,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8ab1adbbefd432f84ccff998e3382de", + "model_id": "d138f67a0d8d44b69b94c79290ab60fa", "version_major": 2, "version_minor": 0 }, @@ -9041,7 +9040,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e5346be07274ca9b3124ad9db4e0a6d", + "model_id": "472095f2f7de47dca63bb953593f60a9", "version_major": 2, "version_minor": 0 }, @@ -9055,7 +9054,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "626e911567f949d5bb8aa78b6b60f934", + "model_id": "d696f828b464421c95874bae379a6fa0", "version_major": 2, "version_minor": 0 }, @@ -9069,7 +9068,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f6d5ce4338784c1cbdaf100334b1407b", + "model_id": "a92efff5798b4681b9d9de929e5f6336", "version_major": 2, "version_minor": 0 }, @@ -9083,7 +9082,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c810e6fd3ceb4568ae78b53fa213914e", + "model_id": "289e5bce5f284ce99e064521ce0e05a9", "version_major": 2, "version_minor": 0 }, @@ -9097,7 +9096,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "afc2246d6e184b538ecfe056dc38f16d", + "model_id": "fdd3f827c9c34a3190aba38b1b13c21f", "version_major": 2, "version_minor": 0 }, @@ -9111,7 +9110,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61255356db634ba984e8844968f4a5e9", + "model_id": "d99ecb21f9e54c9fb262b29adde16bd0", "version_major": 2, "version_minor": 0 }, @@ -9125,7 +9124,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02f1a079b35a44d89dfce052a355c17a", + "model_id": "725a40f03b734c21b8b8e67b3212718f", "version_major": 2, "version_minor": 0 }, @@ -9139,7 +9138,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cb0fb5da2654482ebe196bf0f1751b7f", + "model_id": "640019ee89d047fd8725565835db0eaf", "version_major": 2, "version_minor": 0 }, @@ -9153,7 +9152,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc33b4976a164058bceeb2cfb77eff20", + "model_id": "1580c64b5a874e6aad7a89abacd5cb60", "version_major": 2, "version_minor": 0 }, @@ -9167,7 +9166,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cba3bae12a654da39ea22a391431f2ff", + "model_id": "75eddb18a4c546b1a364977b91a38001", "version_major": 2, "version_minor": 0 }, @@ -9181,7 +9180,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "69f0c0bde038485e8c49934eb6ea819d", + "model_id": "96302a61558d40549b7802f4c44263eb", "version_major": 2, "version_minor": 0 }, @@ -9195,7 +9194,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac8fff761b724c6cb58569db5feeeaa5", + "model_id": "fb7dd989673a4223b66345ef77406828", "version_major": 2, "version_minor": 0 }, @@ -9209,7 +9208,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d0cdf39392d24750901d79aa4bef1a76", + "model_id": "6e74c6d1b7c64e4596a655a0fbaf1bfb", "version_major": 2, "version_minor": 0 }, @@ -9223,7 +9222,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a0d550efbba47d88357781f7b68e073", + "model_id": "d716d0b7820d475ca7653959b52d5450", "version_major": 2, "version_minor": 0 }, @@ -9237,7 +9236,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "286d953141b24199a4253f9fc85b6f82", + "model_id": "137da5f57ba84efdb51aff612b72db01", "version_major": 2, "version_minor": 0 }, @@ -9251,7 +9250,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67cf98d2686948a6b3bd39efb55b3ef4", + "model_id": "bd227089abe34ebda36b2d18df87a86a", "version_major": 2, "version_minor": 0 }, @@ -9265,7 +9264,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "608086150668499e85ab742d06ea4ebd", + "model_id": "044b5721d12d4dadbc68f2e1ef6db71a", "version_major": 2, "version_minor": 0 }, @@ -9279,7 +9278,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "021bf402711e428b97a3e2e96dfcb49e", + "model_id": "cc86818b06a740479b3157b29a1cb18e", "version_major": 2, "version_minor": 0 }, @@ -9293,7 +9292,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0a7bb5628c944b4b570907e91ee7248", + "model_id": "dd634e6fc883498cadcb00911c514371", "version_major": 2, "version_minor": 0 }, @@ -9307,7 +9306,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "46c080135dd7438ea38e749bbe0cc747", + "model_id": "3a52574849754ad5b349a2ef52a33722", "version_major": 2, "version_minor": 0 }, @@ -9321,7 +9320,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4ca53ff274f84b3a9c88ace97834f8c5", + "model_id": "35f1a57c0f6147ce81b036c866a17275", "version_major": 2, "version_minor": 0 }, @@ -9335,7 +9334,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "373a0410ec8444ad957a468cefb21b42", + "model_id": "3cb251ea1f6a4ae7a87fb959e362d392", "version_major": 2, "version_minor": 0 }, @@ -9349,7 +9348,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b67298bbb00b47d290167050e011d20c", + "model_id": "4e12dfd333064cf5a1fffa8ab56f67a6", "version_major": 2, "version_minor": 0 }, @@ -9363,7 +9362,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1dcdd15fe16d401cacf14f9a6f151a17", + "model_id": "73e3283065a94da9b0de1b38f87eb801", "version_major": 2, "version_minor": 0 }, @@ -9377,7 +9376,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "43883ea84b364edea1d9fe08e2db75d8", + "model_id": "d2755e57697a4a01b6514fdba5578bec", "version_major": 2, "version_minor": 0 }, @@ -9391,7 +9390,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e6e2306c5e043cd98d2c5f41d6fba33", + "model_id": "f8f3787f489f46959322dac484b7ed93", "version_major": 2, "version_minor": 0 }, @@ -9405,7 +9404,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31ac9e15ce594f32b36c8898e6500bc9", + "model_id": "f24824825bf644ae831c0e922e16a6c7", "version_major": 2, "version_minor": 0 }, @@ -9419,7 +9418,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6f20edbc41064e44a166abb5c43d4304", + "model_id": "9abde42fb6564285aef92cdc78ac08a4", "version_major": 2, "version_minor": 0 }, @@ -9433,7 +9432,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "2f828a85bd9846be86f63bfe87807643", + "model_id": "debee1aed4cc40d5b303d02eedbdfc41", "version_major": 2, "version_minor": 0 }, @@ -9587,7 +9586,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91acc5ec90d542fd91e2fa1a60c2b472", + "model_id": "49af03e929d149319965f79777b874d7", "version_major": 2, "version_minor": 0 }, @@ -9601,7 +9600,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc002d7048f34829bf0e153de53ec761", + "model_id": "518f016e8418473fa5c367ba4782ea70", "version_major": 2, "version_minor": 0 }, @@ -9615,7 +9614,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c18ba9a746364e29b913561d638c3657", + "model_id": "d5d84c78937e4e86809c6752d2ab9b34", "version_major": 2, "version_minor": 0 }, @@ -9629,7 +9628,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "592ebdfedc0c4e8fadfd0d8070088221", + "model_id": "d0e7561ad6254c608e8ccbe9419bf13d", "version_major": 2, "version_minor": 0 }, @@ -9643,7 +9642,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "6fa0919d758b43ff9156627fab054600", + "model_id": "66c4ce98f5ba4957b673c1969ac1ec6b", "version_major": 2, "version_minor": 0 }, @@ -9797,7 +9796,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80ecf1da2dd340a7aa6136aa43e7655d", + "model_id": "65829521a0704628ad14ef78741bbfc8", "version_major": 2, "version_minor": 0 }, @@ -9811,7 +9810,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4cc8c92b9e224ebeab44e4a14df7179b", + "model_id": "fab5d683ba714ad58a9aab8de4df7b4f", "version_major": 2, "version_minor": 0 }, @@ -9825,7 +9824,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c9fc2259c77a467fb5abc0f4422e8dc9", + "model_id": "e042dbbd1baa44fa9f73d06426c862ef", "version_major": 2, "version_minor": 0 }, @@ -9839,7 +9838,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c0623c653894e629bb4bbab64c0fe0b", + "model_id": "9b23da050f7e4d928362df6059d09c58", "version_major": 2, "version_minor": 0 }, @@ -9853,7 +9852,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f30c449b45314a988d24ef7b456e4422", + "model_id": "b1f1ad4e73bf410497c46c1e3d4ef017", "version_major": 2, "version_minor": 0 }, @@ -9867,7 +9866,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "77ad20ffc8a240acb93bafb56edfa9da", + "model_id": "95951bcd163a48a083e71524e3c8c80f", "version_major": 2, "version_minor": 0 }, @@ -9881,7 +9880,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f26b8c97f5b74aeb837cf5550934dad2", + "model_id": "bfd23af6b76b4c66bb24b6c0af0151db", "version_major": 2, "version_minor": 0 }, @@ -9895,7 +9894,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1b14fe9c7b2640e4a732181e4891d04d", + "model_id": "a620323d378841ffa3fb4e8af6b7b677", "version_major": 2, "version_minor": 0 }, @@ -9909,7 +9908,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebd93c6972e24dcc87f97210c9226ec5", + "model_id": "5c140c009d62482faf2bb955304ddfb5", "version_major": 2, "version_minor": 0 }, @@ -9923,7 +9922,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2b14ad1b9704bd0bc3b958aa8d3122f", + "model_id": "eec96bca2672471fa10edafc612715f9", "version_major": 2, "version_minor": 0 }, @@ -9937,7 +9936,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "11760a605a8c4595a561f565072d3912", + "model_id": "13d227699d96405e9672968a117dd835", "version_major": 2, "version_minor": 0 }, @@ -9951,7 +9950,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "efc5f8437a284a4dbfbc1eababe72f5c", + "model_id": "bdf0d51cecf64b6b88b2189e61e2697d", "version_major": 2, "version_minor": 0 }, @@ -9965,7 +9964,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fca92dc2b3314004b806de3f0476982d", + "model_id": "3663a9cc5dd6492c838b8856ee3db172", "version_major": 2, "version_minor": 0 }, @@ -9979,7 +9978,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c57faff8c244782b4c634632c79fd6d", + "model_id": "5b8a90932c5c43449baddb012d3f8d2f", "version_major": 2, "version_minor": 0 }, @@ -9993,7 +9992,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b1608716da04764a9e79b15ff3b9cd0", + "model_id": "acfe2fcbcbeb40b689a942d4a384e81e", "version_major": 2, "version_minor": 0 }, @@ -10007,7 +10006,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34b6076acc7c46cd85e0580ed62051f7", + "model_id": "a41e9df7b5be41f680c2486bb036eac0", "version_major": 2, "version_minor": 0 }, @@ -10021,7 +10020,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "be9d5f1a1fc1411bac32b804ab4a55c6", + "model_id": "70cfd7e68ad546fca7b8b4fcb5fe112a", "version_major": 2, "version_minor": 0 }, @@ -10035,7 +10034,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15ba55fa813d429d8c096aeab77dac6d", + "model_id": "cc6d28f7b6674cc78526f2a04a232f87", "version_major": 2, "version_minor": 0 }, @@ -10049,7 +10048,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82981797edd24e66a9c7f595777993b3", + "model_id": "ae26f55dfe5d44cd9fae433fb8a3ed46", "version_major": 2, "version_minor": 0 }, @@ -10063,7 +10062,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f497013dca4544cf9027f8c57b9d3862", + "model_id": "b61738f2417244b6b7df1ebb3b3f24d7", "version_major": 2, "version_minor": 0 }, @@ -10077,7 +10076,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18ae423604124c14ad1c546040258830", + "model_id": "fdbf1b51bbd54898b58a2de9f695d5ba", "version_major": 2, "version_minor": 0 }, @@ -10091,7 +10090,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bbcf1925befb44f68ae7c057b87d47fc", + "model_id": "d438be1b459e4f0f90e1a26704baa0f1", "version_major": 2, "version_minor": 0 }, @@ -10105,7 +10104,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3390fcad4b5a4ed7b0962a3c40c08c6c", + "model_id": "164a559d88cd48c58bb43f99cf5819fb", "version_major": 2, "version_minor": 0 }, @@ -10119,7 +10118,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b2672dda8714e74b34df403f44958fb", + "model_id": "e142ec45c48e400bbf4cf7dbc8b94e39", "version_major": 2, "version_minor": 0 }, @@ -10133,7 +10132,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f19a36be4da4760be75497c7125b8f0", + "model_id": "b98239d615c44f9280cd53bf430b38ef", "version_major": 2, "version_minor": 0 }, @@ -10147,7 +10146,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a2a1c7d9d20421e9e92870a6a2e9660", + "model_id": "ee3012ec548c4468ab19394fcc61dfe1", "version_major": 2, "version_minor": 0 }, @@ -10161,7 +10160,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f0286c8429b84e2ab0c163e1b4eef1d0", + "model_id": "59abff92475945148512c124b31620ae", "version_major": 2, "version_minor": 0 }, @@ -10175,7 +10174,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd58ef8e610842f2b301171926de7182", + "model_id": "10742df4535742c8acdcdbea8d8d6a02", "version_major": 2, "version_minor": 0 }, @@ -10189,7 +10188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "539ca6d6148543548ecd3dcf70ed7c11", + "model_id": "7e3d33d2a59f4e2c8413b43cfaee41d1", "version_major": 2, "version_minor": 0 }, @@ -10203,7 +10202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06e7f0d966004f43b3be4b20ccedee56", + "model_id": "1fd03ae4594b4574a55f06d248d8b61a", "version_major": 2, "version_minor": 0 }, @@ -10217,7 +10216,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fda62ae60c6c4677a6ea5636f934dc92", + "model_id": "976c6fee189c41b4b5445895fb180c8d", "version_major": 2, "version_minor": 0 }, @@ -10231,7 +10230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3baf1bb80b444609bbfdea5378d88b16", + "model_id": "198e1fa4f5124895bc0ee188984ea726", "version_major": 2, "version_minor": 0 }, @@ -10245,7 +10244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f668c7e605ea48fb8c8ccc3ce9942537", + "model_id": "c78ca1847595417aa71358a1c1b1a4b0", "version_major": 2, "version_minor": 0 }, @@ -10259,7 +10258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ab646cb72bc4395ab928bd90d12876f", + "model_id": "af7a5a5a341348f2a0be2f3e77656549", "version_major": 2, "version_minor": 0 }, @@ -10273,7 +10272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81a2b942d66144f394a057e50cf01f7c", + "model_id": "b5ab2cc2a47b42828c7a6780a6db2e00", "version_major": 2, "version_minor": 0 }, @@ -10287,7 +10286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2c733eb3a2f40e0b80254de8610baa9", + "model_id": "f972c01912c545948b56ea640af2c5b0", "version_major": 2, "version_minor": 0 }, @@ -10301,7 +10300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3cee07e2b804c9f84e6d1c6985f6eb6", + "model_id": "213b3d4ee66d42e1b57a82d35b720112", "version_major": 2, "version_minor": 0 }, @@ -10315,7 +10314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7a1d5c5a3d2447b99b09d54782e92e4", + "model_id": "ddfc7dcdd1c94636929318d51a61e048", "version_major": 2, "version_minor": 0 }, @@ -10329,7 +10328,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2d6dff1e8e8d492c8020df32915020f6", + "model_id": "3fdce7be7f074ec589421caa0319862f", "version_major": 2, "version_minor": 0 }, @@ -10343,7 +10342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c58bb14675c42759eb18385d3f2c364", + "model_id": "180334cb0f4c4cc6a5f2eeedc55a986b", "version_major": 2, "version_minor": 0 }, @@ -10357,7 +10356,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e846537940449eba9b3df06d6908d2c", + "model_id": "d2e9e0572d4c48bd9dcf25960be64cde", "version_major": 2, "version_minor": 0 }, @@ -10371,7 +10370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eaabc8c0805b4ce38620516da7b5dd2f", + "model_id": "0e26b9ff0a7642e0b8400f0a937eeb30", "version_major": 2, "version_minor": 0 }, @@ -10385,7 +10384,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b54cea4ca634312ac62cef9554c2abd", + "model_id": "bf9537d5986b4aacad134c66a4242b0e", "version_major": 2, "version_minor": 0 }, @@ -10399,7 +10398,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d538586b87440b89506a951ea660c85", + "model_id": "e4ab38c6e45d4628b56bbe5dac9458d7", "version_major": 2, "version_minor": 0 }, @@ -10413,7 +10412,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06ff3bed3b2c44368623e1fe4d16c42d", + "model_id": "18e800771f6f439bb9f15de7edaf8274", "version_major": 2, "version_minor": 0 }, @@ -10427,7 +10426,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6034c4e13c63492399314dd1d0e86900", + "model_id": "ee30326da017481f94e780628f0ec74d", "version_major": 2, "version_minor": 0 }, @@ -10441,7 +10440,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1d2a3ec474f84d7dbd2b1d0811b5679a", + "model_id": "ebaaaf47fecb406facb8804280ed88e2", "version_major": 2, "version_minor": 0 }, @@ -10455,7 +10454,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eedba0809dc948c6b8338bde323e79a5", + "model_id": "ac274611a2704a2ab1468b205859dfc1", "version_major": 2, "version_minor": 0 }, @@ -10469,7 +10468,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b476ab3908ea4301a13f5cc23ff44fa2", + "model_id": "41be5520b1964125872189d604272dc4", "version_major": 2, "version_minor": 0 }, @@ -10483,7 +10482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aff3135a532a4ee88010da77f492cf4d", + "model_id": "666d79b38fcd49c79cac956f8b2f7677", "version_major": 2, "version_minor": 0 }, @@ -10497,7 +10496,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ffe38591306e4351a4228d7f17d91a24", + "model_id": "2253e50224c642a7a56858b1c135213e", "version_major": 2, "version_minor": 0 }, @@ -10511,7 +10510,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2a1a7c1af8fa4e98b60723cbc3766c0b", + "model_id": "f01094a315c4420e9aba161bd526a1e0", "version_major": 2, "version_minor": 0 }, @@ -10525,7 +10524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb73984ee683427195a75edb8a65dfd1", + "model_id": "652fb20eb89245a8ac16173eeb553633", "version_major": 2, "version_minor": 0 }, @@ -10539,7 +10538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fed65b24c3e4cccb75b5e190e192e99", + "model_id": "92757d397a9944e9bf89a627d34d670a", "version_major": 2, "version_minor": 0 }, @@ -10553,7 +10552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40c7a7a509f94bbdb99ff06868239a58", + "model_id": "d4b73d5c186347d9a05f2486c5baafee", "version_major": 2, "version_minor": 0 }, @@ -10567,7 +10566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "043b17d4db7c4a8c92529fac3371ae87", + "model_id": "4daeb823d61246a0aa572689807c117d", "version_major": 2, "version_minor": 0 }, @@ -10581,7 +10580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9340de6a00d54d7e8efb980fb7a63b88", + "model_id": "4da85fb13a6241efa01e2e144209cd5f", "version_major": 2, "version_minor": 0 }, @@ -10595,7 +10594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "69596ddf1da94aed9d62d104734a11ad", + "model_id": "6f4c663a0b6449828fcacf1d77b0fad1", "version_major": 2, "version_minor": 0 }, @@ -10609,7 +10608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83228734f253473ab9b36daa64130a36", + "model_id": "ada51e9d1ee84f7c8d7cda87a9fed382", "version_major": 2, "version_minor": 0 }, @@ -10623,7 +10622,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9fce07d5848d49b9acbd80959f48d055", + "model_id": "035517fa34c84edb8520548a6a6f5f2f", "version_major": 2, "version_minor": 0 }, @@ -10637,7 +10636,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "23ac188be0de4418997c43a01c865381", + "model_id": "ddc0d23180304537a5d5a408daed4159", "version_major": 2, "version_minor": 0 }, @@ -10651,7 +10650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81a145df7fda42b08319ec1963375f71", + "model_id": "c4c0bccb88c34e5da862f7bf33948498", "version_major": 2, "version_minor": 0 }, @@ -10665,7 +10664,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a006b6be707d4cf49143c0588abefcae", + "model_id": "27c5c425ce414190aae3c840ca8df1e0", "version_major": 2, "version_minor": 0 }, @@ -10686,7 +10685,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3a233b74289744058b15d52d3362d9ef", + "model_id": "23414392e3a74a4b8dff5fe1a30f6eec", "version_major": 2, "version_minor": 0 }, @@ -10745,7 +10744,7 @@ }, { "cell_type": "markdown", - "id": "3c78b138", + "id": "fc55d4f9", "metadata": {}, "source": [ "We could also fit a model without the `nn.RNN()` layer by just\n", @@ -10762,7 +10761,7 @@ { "cell_type": "code", "execution_count": 109, - "id": "5586a609", + "id": "c60876d7", "metadata": {}, "outputs": [], "source": [ @@ -10777,7 +10776,7 @@ }, { "cell_type": "markdown", - "id": "c1c361b4", + "id": "c7fc165f", "metadata": {}, "source": [ "Creating a data module follows a familiar pattern." @@ -10786,7 +10785,7 @@ { "cell_type": "code", "execution_count": 110, - "id": "b72ecb13", + "id": "282f7e1c", "metadata": {}, "outputs": [], "source": [ @@ -10799,7 +10798,7 @@ }, { "cell_type": "markdown", - "id": "32016e95", + "id": "310271e1", "metadata": {}, "source": [ "We build a `NonLinearARModel()` that takes as input the 20 features and a hidden layer with 32 units. The remaining steps are familiar." @@ -10808,7 +10807,7 @@ { "cell_type": "code", "execution_count": 111, - "id": "595df68b", + "id": "038fea74", "metadata": {}, "outputs": [], "source": [ @@ -10827,7 +10826,7 @@ { "cell_type": "code", "execution_count": 112, - "id": "e8142533", + "id": "68d309b2", "metadata": {}, "outputs": [], "source": [ @@ -10841,7 +10840,7 @@ }, { "cell_type": "markdown", - "id": "86d4e139", + "id": "998d317c", "metadata": {}, "source": [ "We continue with the usual training steps, fit the model,\n", @@ -10851,7 +10850,7 @@ { "cell_type": "code", "execution_count": 113, - "id": "47bdb236", + "id": "9fc113a1", "metadata": { "lines_to_next_cell": 0 }, @@ -10893,7 +10892,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7abedb50db348d49e14abb6ac8ee82a", + "model_id": "6327380ca56a49628cb311d7382fe96d", "version_major": 2, "version_minor": 0 }, @@ -11194,7 +11193,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ae34f21f0364f47ae24e5b0a0758062", + "model_id": "e9c0c0840a4e4fe3bd9aa771a035ec2a", "version_major": 2, "version_minor": 0 }, @@ -11251,7 +11250,7 @@ }, { "cell_type": "markdown", - "id": "51f435d5", + "id": "66cd716d", "metadata": {}, "source": [ " \n", @@ -11263,8 +11262,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", - "main_language": "python" + "main_language": "python", + "notebook_metadata_filter": "-all" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", diff --git a/Ch11-surv-lab.ipynb b/Ch11-surv-lab.ipynb index 55d07b3..d4c812c 100644 --- a/Ch11-surv-lab.ipynb +++ b/Ch11-surv-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "c7f4eb5a", + "id": "62a1a218", "metadata": {}, "source": [ "\n", @@ -12,7 +12,7 @@ }, { "cell_type": "markdown", - "id": "0ae03bc9", + "id": "9da89fbb", "metadata": {}, "source": [ "# Lab: Survival Analysis\n", @@ -31,13 +31,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "91ac40fd", + "id": "d2d71add", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:33.224953Z", - "iopub.status.busy": "2023-08-07T00:19:33.224846Z", - "iopub.status.idle": "2023-08-07T00:19:34.446999Z", - "shell.execute_reply": "2023-08-07T00:19:34.446629Z" + "iopub.execute_input": "2023-08-21T02:30:04.373618Z", + "iopub.status.busy": "2023-08-21T02:30:04.373522Z", + "iopub.status.idle": "2023-08-21T02:30:05.528375Z", + "shell.execute_reply": "2023-08-21T02:30:05.528065Z" } }, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "markdown", - "id": "a3dbcbbf", + "id": "70fe80b5", "metadata": {}, "source": [ "We also collect the new imports\n", @@ -61,13 +61,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "99782418", + "id": "994efc94", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.448996Z", - "iopub.status.busy": "2023-08-07T00:19:34.448819Z", - "iopub.status.idle": "2023-08-07T00:19:34.539258Z", - "shell.execute_reply": "2023-08-07T00:19:34.538955Z" + "iopub.execute_input": "2023-08-21T02:30:05.530453Z", + "iopub.status.busy": "2023-08-21T02:30:05.530271Z", + "iopub.status.idle": "2023-08-21T02:30:05.593786Z", + "shell.execute_reply": "2023-08-21T02:30:05.593483Z" } }, "outputs": [], @@ -83,7 +83,7 @@ }, { "cell_type": "markdown", - "id": "2c538d28", + "id": "e65a4796", "metadata": {}, "source": [ "## Brain Cancer Data\n", @@ -94,13 +94,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "3137149a", + "id": "9d41ddea", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.541177Z", - "iopub.status.busy": "2023-08-07T00:19:34.541057Z", - "iopub.status.idle": "2023-08-07T00:19:34.547991Z", - "shell.execute_reply": "2023-08-07T00:19:34.547753Z" + "iopub.execute_input": "2023-08-21T02:30:05.595762Z", + "iopub.status.busy": "2023-08-21T02:30:05.595642Z", + "iopub.status.idle": "2023-08-21T02:30:05.602243Z", + "shell.execute_reply": "2023-08-21T02:30:05.601969Z" } }, "outputs": [ @@ -122,7 +122,7 @@ }, { "cell_type": "markdown", - "id": "e798f172", + "id": "4ac65a33", "metadata": {}, "source": [ "The rows index the 88 patients, while the 8 columns contain the predictors and outcome variables.\n", @@ -132,13 +132,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "45963c92", + "id": "2bece782", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.549558Z", - "iopub.status.busy": "2023-08-07T00:19:34.549453Z", - "iopub.status.idle": "2023-08-07T00:19:34.552571Z", - "shell.execute_reply": "2023-08-07T00:19:34.552293Z" + "iopub.execute_input": "2023-08-21T02:30:05.603954Z", + "iopub.status.busy": "2023-08-21T02:30:05.603852Z", + "iopub.status.idle": "2023-08-21T02:30:05.607075Z", + "shell.execute_reply": "2023-08-21T02:30:05.606729Z" }, "lines_to_next_cell": 2 }, @@ -163,13 +163,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "73be61f6", + "id": "9ca465e5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.553962Z", - "iopub.status.busy": "2023-08-07T00:19:34.553866Z", - "iopub.status.idle": "2023-08-07T00:19:34.556544Z", - "shell.execute_reply": "2023-08-07T00:19:34.556286Z" + "iopub.execute_input": "2023-08-21T02:30:05.608553Z", + "iopub.status.busy": "2023-08-21T02:30:05.608445Z", + "iopub.status.idle": "2023-08-21T02:30:05.611386Z", + "shell.execute_reply": "2023-08-21T02:30:05.611134Z" }, "lines_to_next_cell": 2 }, @@ -196,13 +196,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "572f0b9e", + "id": "33bc4d3c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.557984Z", - "iopub.status.busy": "2023-08-07T00:19:34.557901Z", - "iopub.status.idle": "2023-08-07T00:19:34.560759Z", - "shell.execute_reply": "2023-08-07T00:19:34.560490Z" + "iopub.execute_input": "2023-08-21T02:30:05.612735Z", + "iopub.status.busy": "2023-08-21T02:30:05.612639Z", + "iopub.status.idle": "2023-08-21T02:30:05.615164Z", + "shell.execute_reply": "2023-08-21T02:30:05.614915Z" }, "lines_to_next_cell": 2 }, @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "fbd132de", + "id": "eb9c6d4f", "metadata": {}, "source": [ "Before beginning an analysis, it is important to know how the\n", @@ -252,13 +252,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "92c39707", + "id": "0b6dba70", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.562264Z", - "iopub.status.busy": "2023-08-07T00:19:34.562173Z", - "iopub.status.idle": "2023-08-07T00:19:34.764386Z", - "shell.execute_reply": "2023-08-07T00:19:34.763084Z" + "iopub.execute_input": "2023-08-21T02:30:05.616714Z", + "iopub.status.busy": "2023-08-21T02:30:05.616622Z", + "iopub.status.idle": "2023-08-21T02:30:05.728265Z", + "shell.execute_reply": "2023-08-21T02:30:05.727903Z" } }, "outputs": [ @@ -292,7 +292,7 @@ }, { "cell_type": "markdown", - "id": "f037665b", + "id": "2cc511cd", "metadata": {}, "source": [ "Next we create Kaplan-Meier survival curves that are stratified by\n", @@ -318,13 +318,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "3fc7848c", + "id": "9e6f2e70", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.770269Z", - "iopub.status.busy": "2023-08-07T00:19:34.769500Z", - "iopub.status.idle": "2023-08-07T00:19:34.900514Z", - "shell.execute_reply": "2023-08-07T00:19:34.900203Z" + "iopub.execute_input": "2023-08-21T02:30:05.730200Z", + "iopub.status.busy": "2023-08-21T02:30:05.730056Z", + "iopub.status.idle": "2023-08-21T02:30:05.845830Z", + "shell.execute_reply": "2023-08-21T02:30:05.845506Z" } }, "outputs": [ @@ -350,7 +350,7 @@ }, { "cell_type": "markdown", - "id": "c0c1a16a", + "id": "4d7efefb", "metadata": {}, "source": [ "As discussed in Section 11.4, we can perform a\n", @@ -363,13 +363,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "bf30d26f", + "id": "c135f7aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.902462Z", - "iopub.status.busy": "2023-08-07T00:19:34.902313Z", - "iopub.status.idle": "2023-08-07T00:19:34.956077Z", - "shell.execute_reply": "2023-08-07T00:19:34.955714Z" + "iopub.execute_input": "2023-08-21T02:30:05.847658Z", + "iopub.status.busy": "2023-08-21T02:30:05.847519Z", + "iopub.status.idle": "2023-08-21T02:30:05.901295Z", + "shell.execute_reply": "2023-08-21T02:30:05.900935Z" }, "lines_to_next_cell": 2 }, @@ -462,7 +462,7 @@ }, { "cell_type": "markdown", - "id": "e270649c", + "id": "bd14317d", "metadata": {}, "source": [ "The resulting $p$-value is $0.23$, indicating no evidence of a\n", @@ -476,13 +476,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "2ab78e07", + "id": "5f9303dd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.957966Z", - "iopub.status.busy": "2023-08-07T00:19:34.957792Z", - "iopub.status.idle": "2023-08-07T00:19:34.984567Z", - "shell.execute_reply": "2023-08-07T00:19:34.984261Z" + "iopub.execute_input": "2023-08-21T02:30:05.903263Z", + "iopub.status.busy": "2023-08-21T02:30:05.903017Z", + "iopub.status.idle": "2023-08-21T02:30:05.930691Z", + "shell.execute_reply": "2023-08-21T02:30:05.930331Z" } }, "outputs": [ @@ -553,7 +553,7 @@ }, { "cell_type": "markdown", - "id": "b58b93ae", + "id": "7e56e83e", "metadata": {}, "source": [ "The first argument to `fit` should be a data frame containing\n", @@ -569,13 +569,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "4716b7b0", + "id": "bcc8470c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.986336Z", - "iopub.status.busy": "2023-08-07T00:19:34.986193Z", - "iopub.status.idle": "2023-08-07T00:19:34.991518Z", - "shell.execute_reply": "2023-08-07T00:19:34.991252Z" + "iopub.execute_input": "2023-08-21T02:30:05.932434Z", + "iopub.status.busy": "2023-08-21T02:30:05.932285Z", + "iopub.status.idle": "2023-08-21T02:30:05.937796Z", + "shell.execute_reply": "2023-08-21T02:30:05.937549Z" } }, "outputs": [ @@ -659,7 +659,7 @@ }, { "cell_type": "markdown", - "id": "2820f486", + "id": "2e6163ca", "metadata": {}, "source": [ "Regardless of which test we use, we see that there is no clear\n", @@ -675,13 +675,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "c2767d88", + "id": "c26a3499", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:34.993223Z", - "iopub.status.busy": "2023-08-07T00:19:34.993093Z", - "iopub.status.idle": "2023-08-07T00:19:35.028673Z", - "shell.execute_reply": "2023-08-07T00:19:35.028408Z" + "iopub.execute_input": "2023-08-21T02:30:05.939300Z", + "iopub.status.busy": "2023-08-21T02:30:05.939184Z", + "iopub.status.idle": "2023-08-21T02:30:05.979585Z", + "shell.execute_reply": "2023-08-21T02:30:05.979250Z" } }, "outputs": [ @@ -800,7 +800,7 @@ }, { "cell_type": "markdown", - "id": "eee4ab1f", + "id": "a96e311c", "metadata": {}, "source": [ " The `diagnosis` variable has been coded so that the baseline\n", @@ -823,13 +823,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "ede1d219", + "id": "8d999f26", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.030313Z", - "iopub.status.busy": "2023-08-07T00:19:35.030211Z", - "iopub.status.idle": "2023-08-07T00:19:35.034142Z", - "shell.execute_reply": "2023-08-07T00:19:35.033836Z" + "iopub.execute_input": "2023-08-21T02:30:05.981441Z", + "iopub.status.busy": "2023-08-21T02:30:05.981315Z", + "iopub.status.idle": "2023-08-21T02:30:05.986317Z", + "shell.execute_reply": "2023-08-21T02:30:05.985949Z" } }, "outputs": [], @@ -845,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "e1c307ae", + "id": "bf628fd8", "metadata": {}, "source": [ "We make four\n", @@ -856,13 +856,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "dc032a71", + "id": "a1f6b355", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.035583Z", - "iopub.status.busy": "2023-08-07T00:19:35.035483Z", - "iopub.status.idle": "2023-08-07T00:19:35.041790Z", - "shell.execute_reply": "2023-08-07T00:19:35.041394Z" + "iopub.execute_input": "2023-08-21T02:30:05.988012Z", + "iopub.status.busy": "2023-08-21T02:30:05.987898Z", + "iopub.status.idle": "2023-08-21T02:30:05.993889Z", + "shell.execute_reply": "2023-08-21T02:30:05.993534Z" } }, "outputs": [ @@ -974,7 +974,7 @@ }, { "cell_type": "markdown", - "id": "84da2586", + "id": "3f6334e4", "metadata": {}, "source": [ "We then construct the model matrix based on the model specification `all_MS` used to fit\n", @@ -984,13 +984,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "e7c1fe43", + "id": "a1a9d5b3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.043454Z", - "iopub.status.busy": "2023-08-07T00:19:35.043346Z", - "iopub.status.idle": "2023-08-07T00:19:35.050931Z", - "shell.execute_reply": "2023-08-07T00:19:35.050643Z" + "iopub.execute_input": "2023-08-21T02:30:05.995682Z", + "iopub.status.busy": "2023-08-21T02:30:05.995549Z", + "iopub.status.idle": "2023-08-21T02:30:06.005479Z", + "shell.execute_reply": "2023-08-21T02:30:06.005089Z" } }, "outputs": [ @@ -1117,7 +1117,7 @@ }, { "cell_type": "markdown", - "id": "3cfe1ec4", + "id": "3eaec7e6", "metadata": {}, "source": [ "We can use the `predict_survival_function()` method to obtain the estimated survival function." @@ -1126,13 +1126,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "f89fbed7", + "id": "1a18b56a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.052472Z", - "iopub.status.busy": "2023-08-07T00:19:35.052367Z", - "iopub.status.idle": "2023-08-07T00:19:35.059232Z", - "shell.execute_reply": "2023-08-07T00:19:35.058922Z" + "iopub.execute_input": "2023-08-21T02:30:06.007172Z", + "iopub.status.busy": "2023-08-21T02:30:06.007049Z", + "iopub.status.idle": "2023-08-21T02:30:06.014185Z", + "shell.execute_reply": "2023-08-21T02:30:06.013870Z" }, "lines_to_next_cell": 0 }, @@ -1276,7 +1276,7 @@ }, { "cell_type": "markdown", - "id": "29afd641", + "id": "7d533f90", "metadata": {}, "source": [ "This returns a data frame,\n", @@ -1287,13 +1287,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "8f0329b4", + "id": "ff3de29c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.061046Z", - "iopub.status.busy": "2023-08-07T00:19:35.060930Z", - "iopub.status.idle": "2023-08-07T00:19:35.167601Z", - "shell.execute_reply": "2023-08-07T00:19:35.167288Z" + "iopub.execute_input": "2023-08-21T02:30:06.015778Z", + "iopub.status.busy": "2023-08-21T02:30:06.015664Z", + "iopub.status.idle": "2023-08-21T02:30:06.124035Z", + "shell.execute_reply": "2023-08-21T02:30:06.123732Z" }, "lines_to_next_cell": 2 }, @@ -1316,7 +1316,7 @@ }, { "cell_type": "markdown", - "id": "12723ce5", + "id": "e660793e", "metadata": {}, "source": [ "## Publication Data\n", @@ -1330,13 +1330,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "3045bfc0", + "id": "cd9060c1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.169251Z", - "iopub.status.busy": "2023-08-07T00:19:35.169133Z", - "iopub.status.idle": "2023-08-07T00:19:35.287186Z", - "shell.execute_reply": "2023-08-07T00:19:35.286859Z" + "iopub.execute_input": "2023-08-21T02:30:06.125714Z", + "iopub.status.busy": "2023-08-21T02:30:06.125592Z", + "iopub.status.idle": "2023-08-21T02:30:06.243701Z", + "shell.execute_reply": "2023-08-21T02:30:06.243300Z" } }, "outputs": [ @@ -1363,7 +1363,7 @@ }, { "cell_type": "markdown", - "id": "6fcb22f7", + "id": "d8f0f687", "metadata": {}, "source": [ "As discussed previously, the $p$-values from fitting Cox’s\n", @@ -1375,13 +1375,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "d070f716", + "id": "6af7106e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.288887Z", - "iopub.status.busy": "2023-08-07T00:19:35.288769Z", - "iopub.status.idle": "2023-08-07T00:19:35.321428Z", - "shell.execute_reply": "2023-08-07T00:19:35.321128Z" + "iopub.execute_input": "2023-08-21T02:30:06.245493Z", + "iopub.status.busy": "2023-08-21T02:30:06.245357Z", + "iopub.status.idle": "2023-08-21T02:30:06.281521Z", + "shell.execute_reply": "2023-08-21T02:30:06.281138Z" }, "lines_to_next_cell": 2 }, @@ -1453,7 +1453,7 @@ }, { "cell_type": "markdown", - "id": "513a55b1", + "id": "8ef28b8b", "metadata": {}, "source": [ "However, the results change dramatically when we include other\n", @@ -1464,13 +1464,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "2bbcdd0c", + "id": "b6ebefa7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.323119Z", - "iopub.status.busy": "2023-08-07T00:19:35.323003Z", - "iopub.status.idle": "2023-08-07T00:19:35.362910Z", - "shell.execute_reply": "2023-08-07T00:19:35.362438Z" + "iopub.execute_input": "2023-08-21T02:30:06.283282Z", + "iopub.status.busy": "2023-08-21T02:30:06.283123Z", + "iopub.status.idle": "2023-08-21T02:30:06.327003Z", + "shell.execute_reply": "2023-08-21T02:30:06.326646Z" } }, "outputs": [ @@ -1573,7 +1573,7 @@ }, { "cell_type": "markdown", - "id": "75bb8aa6", + "id": "d463e623", "metadata": {}, "source": [ "We see that there are a number of statistically significant variables,\n", @@ -1583,7 +1583,7 @@ }, { "cell_type": "markdown", - "id": "bfe236e5", + "id": "a23c38e0", "metadata": {}, "source": [ "## Call Center Data\n", @@ -1608,13 +1608,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "b8ece43a", + "id": "098f42ea", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.364905Z", - "iopub.status.busy": "2023-08-07T00:19:35.364772Z", - "iopub.status.idle": "2023-08-07T00:19:35.368589Z", - "shell.execute_reply": "2023-08-07T00:19:35.368291Z" + "iopub.execute_input": "2023-08-21T02:30:06.329058Z", + "iopub.status.busy": "2023-08-21T02:30:06.328927Z", + "iopub.status.idle": "2023-08-21T02:30:06.332782Z", + "shell.execute_reply": "2023-08-21T02:30:06.332425Z" } }, "outputs": [], @@ -1637,7 +1637,7 @@ }, { "cell_type": "markdown", - "id": "c93e44f3", + "id": "2f54ed03", "metadata": {}, "source": [ "We then build a model matrix (omitting the intercept)" @@ -1646,13 +1646,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "3e4f766f", + "id": "26d5d0d0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.370485Z", - "iopub.status.busy": "2023-08-07T00:19:35.370371Z", - "iopub.status.idle": "2023-08-07T00:19:35.377790Z", - "shell.execute_reply": "2023-08-07T00:19:35.377469Z" + "iopub.execute_input": "2023-08-21T02:30:06.334692Z", + "iopub.status.busy": "2023-08-21T02:30:06.334589Z", + "iopub.status.idle": "2023-08-21T02:30:06.344047Z", + "shell.execute_reply": "2023-08-21T02:30:06.343708Z" } }, "outputs": [], @@ -1666,7 +1666,7 @@ }, { "cell_type": "markdown", - "id": "cad1ed19", + "id": "a1a8f65d", "metadata": {}, "source": [ "It is worthwhile to take a peek at the model matrix `X`, so\n", @@ -1678,13 +1678,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "72f42d14", + "id": "77500663", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.380244Z", - "iopub.status.busy": "2023-08-07T00:19:35.380068Z", - "iopub.status.idle": "2023-08-07T00:19:35.384542Z", - "shell.execute_reply": "2023-08-07T00:19:35.384259Z" + "iopub.execute_input": "2023-08-21T02:30:06.345660Z", + "iopub.status.busy": "2023-08-21T02:30:06.345575Z", + "iopub.status.idle": "2023-08-21T02:30:06.350086Z", + "shell.execute_reply": "2023-08-21T02:30:06.349797Z" } }, "outputs": [ @@ -1781,7 +1781,7 @@ }, { "cell_type": "markdown", - "id": "38c40ae1", + "id": "fd7bd61c", "metadata": {}, "source": [ "Next, we specify the coefficients and the hazard function." @@ -1790,13 +1790,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "8b921536", + "id": "74324a56", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.386034Z", - "iopub.status.busy": "2023-08-07T00:19:35.385942Z", - "iopub.status.idle": "2023-08-07T00:19:35.420461Z", - "shell.execute_reply": "2023-08-07T00:19:35.405608Z" + "iopub.execute_input": "2023-08-21T02:30:06.351738Z", + "iopub.status.busy": "2023-08-21T02:30:06.351549Z", + "iopub.status.idle": "2023-08-21T02:30:06.444268Z", + "shell.execute_reply": "2023-08-21T02:30:06.441484Z" } }, "outputs": [], @@ -1808,7 +1808,7 @@ }, { "cell_type": "markdown", - "id": "a0698ffd", + "id": "cfe879e6", "metadata": {}, "source": [ "Here, we have set the coefficient associated with `Operators` to\n", @@ -1837,13 +1837,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "96ce0f99", + "id": "d4be10c2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.484657Z", - "iopub.status.busy": "2023-08-07T00:19:35.484150Z", - "iopub.status.idle": "2023-08-07T00:19:35.509286Z", - "shell.execute_reply": "2023-08-07T00:19:35.508169Z" + "iopub.execute_input": "2023-08-21T02:30:06.449822Z", + "iopub.status.busy": "2023-08-21T02:30:06.449515Z", + "iopub.status.idle": "2023-08-21T02:30:06.458388Z", + "shell.execute_reply": "2023-08-21T02:30:06.457673Z" }, "lines_to_next_cell": 0 }, @@ -1854,7 +1854,7 @@ }, { "cell_type": "markdown", - "id": "1956e4c2", + "id": "6095cfc1", "metadata": {}, "source": [ "We are now ready to generate data under the Cox proportional hazards\n", @@ -1868,13 +1868,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "63d78ff9", + "id": "c98d396f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.522637Z", - "iopub.status.busy": "2023-08-07T00:19:35.522260Z", - "iopub.status.idle": "2023-08-07T00:19:35.630452Z", - "shell.execute_reply": "2023-08-07T00:19:35.627960Z" + "iopub.execute_input": "2023-08-21T02:30:06.461931Z", + "iopub.status.busy": "2023-08-21T02:30:06.461787Z", + "iopub.status.idle": "2023-08-21T02:30:06.624349Z", + "shell.execute_reply": "2023-08-21T02:30:06.624026Z" } }, "outputs": [], @@ -1886,7 +1886,7 @@ }, { "cell_type": "markdown", - "id": "035e4ecf", + "id": "ed2e23ea", "metadata": {}, "source": [ "We now simulate our censoring variable, for which we assume\n", @@ -1897,13 +1897,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "fe008dbf", + "id": "caf627bc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.635844Z", - "iopub.status.busy": "2023-08-07T00:19:35.635469Z", - "iopub.status.idle": "2023-08-07T00:19:35.649527Z", - "shell.execute_reply": "2023-08-07T00:19:35.646191Z" + "iopub.execute_input": "2023-08-21T02:30:06.626165Z", + "iopub.status.busy": "2023-08-21T02:30:06.626054Z", + "iopub.status.idle": "2023-08-21T02:30:06.630808Z", + "shell.execute_reply": "2023-08-21T02:30:06.630542Z" }, "lines_to_next_cell": 2 }, @@ -2005,13 +2005,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "c3a2bec7", + "id": "e63242f9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.653001Z", - "iopub.status.busy": "2023-08-07T00:19:35.652338Z", - "iopub.status.idle": "2023-08-07T00:19:35.656830Z", - "shell.execute_reply": "2023-08-07T00:19:35.656500Z" + "iopub.execute_input": "2023-08-21T02:30:06.632357Z", + "iopub.status.busy": "2023-08-21T02:30:06.632261Z", + "iopub.status.idle": "2023-08-21T02:30:06.634630Z", + "shell.execute_reply": "2023-08-21T02:30:06.634305Z" } }, "outputs": [ @@ -2032,7 +2032,7 @@ }, { "cell_type": "markdown", - "id": "207937e5", + "id": "5f345011", "metadata": {}, "source": [ "We now plot Kaplan-Meier survival curves. First, we stratify by `Center`." @@ -2041,13 +2041,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "2b27af56", + "id": "338db71d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.658421Z", - "iopub.status.busy": "2023-08-07T00:19:35.658328Z", - "iopub.status.idle": "2023-08-07T00:19:35.811796Z", - "shell.execute_reply": "2023-08-07T00:19:35.811449Z" + "iopub.execute_input": "2023-08-21T02:30:06.636188Z", + "iopub.status.busy": "2023-08-21T02:30:06.636081Z", + "iopub.status.idle": "2023-08-21T02:30:06.791856Z", + "shell.execute_reply": "2023-08-21T02:30:06.791521Z" } }, "outputs": [ @@ -2084,7 +2084,7 @@ }, { "cell_type": "markdown", - "id": "be6d37f7", + "id": "1b5a1230", "metadata": {}, "source": [ "Next, we stratify by `Time`." @@ -2093,13 +2093,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "9625598d", + "id": "c1db6e15", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:35.813696Z", - "iopub.status.busy": "2023-08-07T00:19:35.813601Z", - "iopub.status.idle": "2023-08-07T00:19:36.041021Z", - "shell.execute_reply": "2023-08-07T00:19:36.040708Z" + "iopub.execute_input": "2023-08-21T02:30:06.793629Z", + "iopub.status.busy": "2023-08-21T02:30:06.793538Z", + "iopub.status.idle": "2023-08-21T02:30:06.992155Z", + "shell.execute_reply": "2023-08-21T02:30:06.991803Z" } }, "outputs": [ @@ -2136,7 +2136,7 @@ }, { "cell_type": "markdown", - "id": "1408ebc0", + "id": "deb73d38", "metadata": {}, "source": [ "It seems that calls at Call Center B take longer to be answered than\n", @@ -2149,13 +2149,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "75a744ef", + "id": "02ea4212", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:36.043079Z", - "iopub.status.busy": "2023-08-07T00:19:36.042900Z", - "iopub.status.idle": "2023-08-07T00:19:36.061936Z", - "shell.execute_reply": "2023-08-07T00:19:36.061630Z" + "iopub.execute_input": "2023-08-21T02:30:06.993929Z", + "iopub.status.busy": "2023-08-21T02:30:06.993819Z", + "iopub.status.idle": "2023-08-21T02:30:07.011557Z", + "shell.execute_reply": "2023-08-21T02:30:07.011276Z" }, "lines_to_next_cell": 2 }, @@ -2247,7 +2247,7 @@ }, { "cell_type": "markdown", - "id": "be5055e4", + "id": "db9cc6ee", "metadata": {}, "source": [ "Next, we consider the effect of `Time`." @@ -2256,13 +2256,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "9badb3e3", + "id": "0ac610d5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:36.063627Z", - "iopub.status.busy": "2023-08-07T00:19:36.063519Z", - "iopub.status.idle": "2023-08-07T00:19:36.082451Z", - "shell.execute_reply": "2023-08-07T00:19:36.082161Z" + "iopub.execute_input": "2023-08-21T02:30:07.013331Z", + "iopub.status.busy": "2023-08-21T02:30:07.013187Z", + "iopub.status.idle": "2023-08-21T02:30:07.030401Z", + "shell.execute_reply": "2023-08-21T02:30:07.030073Z" }, "lines_to_next_cell": 2 }, @@ -2354,7 +2354,7 @@ }, { "cell_type": "markdown", - "id": "64b2bc33", + "id": "0946d3ef", "metadata": {}, "source": [ "As in the case of a categorical variable with 2 levels, these\n", @@ -2366,13 +2366,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "026e9ff8", + "id": "107cedad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:36.084076Z", - "iopub.status.busy": "2023-08-07T00:19:36.083964Z", - "iopub.status.idle": "2023-08-07T00:19:36.208409Z", - "shell.execute_reply": "2023-08-07T00:19:36.208076Z" + "iopub.execute_input": "2023-08-21T02:30:07.032008Z", + "iopub.status.busy": "2023-08-21T02:30:07.031887Z", + "iopub.status.idle": "2023-08-21T02:30:07.160931Z", + "shell.execute_reply": "2023-08-21T02:30:07.160639Z" }, "lines_to_next_cell": 2 }, @@ -2462,7 +2462,7 @@ }, { "cell_type": "markdown", - "id": "4ed54fe0", + "id": "10f2a0c1", "metadata": {}, "source": [ "Next, we look at the results for `Time`." @@ -2471,13 +2471,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "7cab3789", + "id": "334eb331", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:36.210101Z", - "iopub.status.busy": "2023-08-07T00:19:36.209985Z", - "iopub.status.idle": "2023-08-07T00:19:36.334146Z", - "shell.execute_reply": "2023-08-07T00:19:36.333801Z" + "iopub.execute_input": "2023-08-21T02:30:07.162793Z", + "iopub.status.busy": "2023-08-21T02:30:07.162651Z", + "iopub.status.idle": "2023-08-21T02:30:07.291875Z", + "shell.execute_reply": "2023-08-21T02:30:07.291550Z" }, "lines_to_next_cell": 2 }, @@ -2567,7 +2567,7 @@ }, { "cell_type": "markdown", - "id": "2d250dc9", + "id": "774963d4", "metadata": {}, "source": [ "We find that differences between centers are highly significant, as\n", @@ -2579,13 +2579,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "5cc4b898", + "id": "421811c5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:36.336025Z", - "iopub.status.busy": "2023-08-07T00:19:36.335898Z", - "iopub.status.idle": "2023-08-07T00:19:36.561174Z", - "shell.execute_reply": "2023-08-07T00:19:36.559597Z" + "iopub.execute_input": "2023-08-21T02:30:07.293545Z", + "iopub.status.busy": "2023-08-21T02:30:07.293433Z", + "iopub.status.idle": "2023-08-21T02:30:07.532213Z", + "shell.execute_reply": "2023-08-21T02:30:07.531293Z" }, "lines_to_next_cell": 2 }, @@ -2684,7 +2684,7 @@ }, { "cell_type": "markdown", - "id": "bec9d61d", + "id": "3c65063f", "metadata": {}, "source": [ "The $p$-values for Center B and evening time\n", diff --git a/Ch12-unsup-lab.Rmd b/Ch12-unsup-lab.Rmd index 5af8e75..8f885f4 100644 --- a/Ch12-unsup-lab.Rmd +++ b/Ch12-unsup-lab.Rmd @@ -14,7 +14,7 @@ jupyter: # Chapter 12 - # Lab: Unsupervised Learning +# Lab: Unsupervised Learning In this lab we demonstrate PCA and clustering on several datasets. As in other labs, we import some of our libraries at this top level. This makes the code more readable, as scanning the first few @@ -819,7 +819,7 @@ linkage. Clearly cell lines within a single cancer type do tend to cluster together, although the clustering is not perfect. We will use complete linkage hierarchical clustering for the analysis that follows. - + We can cut the dendrogram at the height that will yield a particular number of clusters, say four: diff --git a/Ch12-unsup-lab.ipynb b/Ch12-unsup-lab.ipynb index 6fb862b..54b3379 100644 --- a/Ch12-unsup-lab.ipynb +++ b/Ch12-unsup-lab.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "markdown", - "id": "80f16ff6", + "id": "b1c6a137", "metadata": {}, "source": [ "\n", "# Chapter 12\n", "\n", - " # Lab: Unsupervised Learning\n", + "# Lab: Unsupervised Learning\n", "In this lab we demonstrate PCA and clustering on several datasets.\n", "As in other labs, we import some of our libraries at this top\n", "level. This makes the code more readable, as scanning the first few\n", @@ -19,13 +19,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "24559be0", + "id": "6d5ba583", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:37.853212Z", - "iopub.status.busy": "2023-08-07T00:19:37.853110Z", - "iopub.status.idle": "2023-08-07T00:19:38.840274Z", - "shell.execute_reply": "2023-08-07T00:19:38.839771Z" + "iopub.execute_input": "2023-08-21T02:30:08.846762Z", + "iopub.status.busy": "2023-08-21T02:30:08.846653Z", + "iopub.status.idle": "2023-08-21T02:30:09.939364Z", + "shell.execute_reply": "2023-08-21T02:30:09.939004Z" }, "lines_to_next_cell": 0 }, @@ -42,7 +42,7 @@ }, { "cell_type": "markdown", - "id": "59b24a4b", + "id": "d01ab033", "metadata": {}, "source": [ "We also collect the new imports\n", @@ -52,13 +52,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "06fff57d", + "id": "64c83257", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:38.842722Z", - "iopub.status.busy": "2023-08-07T00:19:38.842335Z", - "iopub.status.idle": "2023-08-07T00:19:39.050690Z", - "shell.execute_reply": "2023-08-07T00:19:39.050367Z" + "iopub.execute_input": "2023-08-21T02:30:09.941473Z", + "iopub.status.busy": "2023-08-21T02:30:09.941296Z", + "iopub.status.idle": "2023-08-21T02:30:10.036632Z", + "shell.execute_reply": "2023-08-21T02:30:10.036163Z" } }, "outputs": [], @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "f091de06", + "id": "da4201dc", "metadata": {}, "source": [ "## Principal Components Analysis\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "f425e07e", + "id": "04ec4481", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:39.052503Z", - "iopub.status.busy": "2023-08-07T00:19:39.052392Z", - "iopub.status.idle": "2023-08-07T00:19:40.034559Z", - "shell.execute_reply": "2023-08-07T00:19:40.034237Z" + "iopub.execute_input": "2023-08-21T02:30:10.038974Z", + "iopub.status.busy": "2023-08-21T02:30:10.038734Z", + "iopub.status.idle": "2023-08-21T02:30:11.214222Z", + "shell.execute_reply": "2023-08-21T02:30:11.213910Z" } }, "outputs": [ @@ -547,7 +547,7 @@ }, { "cell_type": "markdown", - "id": "e2942890", + "id": "02805a1a", "metadata": {}, "source": [ "The columns of the data set contain the four variables." @@ -556,13 +556,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "b127d014", + "id": "1b66036a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.036610Z", - "iopub.status.busy": "2023-08-07T00:19:40.036476Z", - "iopub.status.idle": "2023-08-07T00:19:40.039012Z", - "shell.execute_reply": "2023-08-07T00:19:40.038760Z" + "iopub.execute_input": "2023-08-21T02:30:11.216028Z", + "iopub.status.busy": "2023-08-21T02:30:11.215902Z", + "iopub.status.idle": "2023-08-21T02:30:11.218553Z", + "shell.execute_reply": "2023-08-21T02:30:11.218232Z" } }, "outputs": [ @@ -583,7 +583,7 @@ }, { "cell_type": "markdown", - "id": "68718331", + "id": "3decadf1", "metadata": {}, "source": [ "We first briefly examine the data. We notice that the variables have vastly different means." @@ -592,13 +592,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "c7343f72", + "id": "52e900fd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.040518Z", - "iopub.status.busy": "2023-08-07T00:19:40.040415Z", - "iopub.status.idle": "2023-08-07T00:19:40.043628Z", - "shell.execute_reply": "2023-08-07T00:19:40.043271Z" + "iopub.execute_input": "2023-08-21T02:30:11.220090Z", + "iopub.status.busy": "2023-08-21T02:30:11.219971Z", + "iopub.status.idle": "2023-08-21T02:30:11.223332Z", + "shell.execute_reply": "2023-08-21T02:30:11.223004Z" }, "lines_to_next_cell": 2 }, @@ -624,7 +624,7 @@ }, { "cell_type": "markdown", - "id": "171a1ee0", + "id": "27d011da", "metadata": {}, "source": [ "Dataframes have several useful methods for computing\n", @@ -635,13 +635,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "34501140", + "id": "68684f78", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.045538Z", - "iopub.status.busy": "2023-08-07T00:19:40.045415Z", - "iopub.status.idle": "2023-08-07T00:19:40.048692Z", - "shell.execute_reply": "2023-08-07T00:19:40.048363Z" + "iopub.execute_input": "2023-08-21T02:30:11.225020Z", + "iopub.status.busy": "2023-08-21T02:30:11.224913Z", + "iopub.status.idle": "2023-08-21T02:30:11.228160Z", + "shell.execute_reply": "2023-08-21T02:30:11.227843Z" } }, "outputs": [ @@ -666,7 +666,7 @@ }, { "cell_type": "markdown", - "id": "5634db88", + "id": "ef3003e0", "metadata": {}, "source": [ "Not surprisingly, the variables also have vastly different variances.\n", @@ -689,13 +689,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "daf119e8", + "id": "d2b7caf9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.050181Z", - "iopub.status.busy": "2023-08-07T00:19:40.050071Z", - "iopub.status.idle": "2023-08-07T00:19:40.053916Z", - "shell.execute_reply": "2023-08-07T00:19:40.053514Z" + "iopub.execute_input": "2023-08-21T02:30:11.229847Z", + "iopub.status.busy": "2023-08-21T02:30:11.229717Z", + "iopub.status.idle": "2023-08-21T02:30:11.233108Z", + "shell.execute_reply": "2023-08-21T02:30:11.232828Z" }, "lines_to_next_cell": 0 }, @@ -708,7 +708,7 @@ }, { "cell_type": "markdown", - "id": "dd6e5f5b", + "id": "54c90b5c", "metadata": {}, "source": [ "Having scaled the data, we can then\n", @@ -719,13 +719,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "a0eda7c9", + "id": "de8f57fa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.055993Z", - "iopub.status.busy": "2023-08-07T00:19:40.055896Z", - "iopub.status.idle": "2023-08-07T00:19:40.057688Z", - "shell.execute_reply": "2023-08-07T00:19:40.057406Z" + "iopub.execute_input": "2023-08-21T02:30:11.234711Z", + "iopub.status.busy": "2023-08-21T02:30:11.234595Z", + "iopub.status.idle": "2023-08-21T02:30:11.241074Z", + "shell.execute_reply": "2023-08-21T02:30:11.239548Z" }, "lines_to_next_cell": 0 }, @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "e5758ee5", + "id": "42bd0a96", "metadata": {}, "source": [ "(By default, the `PCA()` transform centers the variables to have\n", @@ -748,13 +748,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "1430fb3c", + "id": "26c45f1e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.059093Z", - "iopub.status.busy": "2023-08-07T00:19:40.059009Z", - "iopub.status.idle": "2023-08-07T00:19:40.062720Z", - "shell.execute_reply": "2023-08-07T00:19:40.062341Z" + "iopub.execute_input": "2023-08-21T02:30:11.242595Z", + "iopub.status.busy": "2023-08-21T02:30:11.242489Z", + "iopub.status.idle": "2023-08-21T02:30:11.246323Z", + "shell.execute_reply": "2023-08-21T02:30:11.246064Z" } }, "outputs": [ @@ -778,7 +778,7 @@ }, { "cell_type": "markdown", - "id": "56890b11", + "id": "a68b81f5", "metadata": {}, "source": [ "After fitting, the `mean_` attribute corresponds to the means\n", @@ -789,13 +789,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "6131d8d1", + "id": "3097e99d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.064409Z", - "iopub.status.busy": "2023-08-07T00:19:40.064295Z", - "iopub.status.idle": "2023-08-07T00:19:40.066520Z", - "shell.execute_reply": "2023-08-07T00:19:40.066266Z" + "iopub.execute_input": "2023-08-21T02:30:11.247882Z", + "iopub.status.busy": "2023-08-21T02:30:11.247777Z", + "iopub.status.idle": "2023-08-21T02:30:11.250076Z", + "shell.execute_reply": "2023-08-21T02:30:11.249792Z" } }, "outputs": [ @@ -816,7 +816,7 @@ }, { "cell_type": "markdown", - "id": "3c1c34e7", + "id": "0c81e463", "metadata": {}, "source": [ "The scores can be computed using the `transform()` method\n", @@ -826,13 +826,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "08246aad", + "id": "c071a242", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.067963Z", - "iopub.status.busy": "2023-08-07T00:19:40.067854Z", - "iopub.status.idle": "2023-08-07T00:19:40.069743Z", - "shell.execute_reply": "2023-08-07T00:19:40.069498Z" + "iopub.execute_input": "2023-08-21T02:30:11.251690Z", + "iopub.status.busy": "2023-08-21T02:30:11.251589Z", + "iopub.status.idle": "2023-08-21T02:30:11.253710Z", + "shell.execute_reply": "2023-08-21T02:30:11.253213Z" }, "lines_to_next_cell": 0 }, @@ -843,7 +843,7 @@ }, { "cell_type": "markdown", - "id": "574ed47f", + "id": "84581683", "metadata": {}, "source": [ "We will plot these scores a bit further down.\n", @@ -855,13 +855,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "b682b632", + "id": "c9bcab06", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.071367Z", - "iopub.status.busy": "2023-08-07T00:19:40.071220Z", - "iopub.status.idle": "2023-08-07T00:19:40.073516Z", - "shell.execute_reply": "2023-08-07T00:19:40.073257Z" + "iopub.execute_input": "2023-08-21T02:30:11.255475Z", + "iopub.status.busy": "2023-08-21T02:30:11.255331Z", + "iopub.status.idle": "2023-08-21T02:30:11.257666Z", + "shell.execute_reply": "2023-08-21T02:30:11.257404Z" } }, "outputs": [ @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "fc8bfc99", + "id": "9dec4ca1", "metadata": {}, "source": [ "The `biplot` is a common visualization method used with\n", @@ -898,13 +898,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "c165e990", + "id": "7375ab13", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.075045Z", - "iopub.status.busy": "2023-08-07T00:19:40.074930Z", - "iopub.status.idle": "2023-08-07T00:19:40.260577Z", - "shell.execute_reply": "2023-08-07T00:19:40.259910Z" + "iopub.execute_input": "2023-08-21T02:30:11.259208Z", + "iopub.status.busy": "2023-08-21T02:30:11.259106Z", + "iopub.status.idle": "2023-08-21T02:30:11.411821Z", + "shell.execute_reply": "2023-08-21T02:30:11.411424Z" }, "lines_to_next_cell": 0 }, @@ -935,7 +935,7 @@ }, { "cell_type": "markdown", - "id": "eaef3e98", + "id": "4dacec4b", "metadata": {}, "source": [ "Notice that this figure is a reflection of Figure 12.1 through the $y$-axis. Recall that the\n", @@ -948,13 +948,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "848c9f35", + "id": "4c1988de", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.266938Z", - "iopub.status.busy": "2023-08-07T00:19:40.266653Z", - "iopub.status.idle": "2023-08-07T00:19:40.403287Z", - "shell.execute_reply": "2023-08-07T00:19:40.402947Z" + "iopub.execute_input": "2023-08-21T02:30:11.413868Z", + "iopub.status.busy": "2023-08-21T02:30:11.413708Z", + "iopub.status.idle": "2023-08-21T02:30:11.510731Z", + "shell.execute_reply": "2023-08-21T02:30:11.510392Z" } }, "outputs": [ @@ -986,7 +986,7 @@ }, { "cell_type": "markdown", - "id": "e380e98d", + "id": "f96f4f26", "metadata": {}, "source": [ "The standard deviations of the principal component scores are as follows:" @@ -995,13 +995,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "34fdfe21", + "id": "965c6320", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.404992Z", - "iopub.status.busy": "2023-08-07T00:19:40.404880Z", - "iopub.status.idle": "2023-08-07T00:19:40.407457Z", - "shell.execute_reply": "2023-08-07T00:19:40.407174Z" + "iopub.execute_input": "2023-08-21T02:30:11.512534Z", + "iopub.status.busy": "2023-08-21T02:30:11.512382Z", + "iopub.status.idle": "2023-08-21T02:30:11.515359Z", + "shell.execute_reply": "2023-08-21T02:30:11.514710Z" }, "lines_to_next_cell": 2 }, @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "c24b8ca2", + "id": "0aa2cebf", "metadata": {}, "source": [ "The variance of each score can be extracted directly from the `pcaUS` object via\n", @@ -1033,13 +1033,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "31b43c57", + "id": "cd5e1663", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.409206Z", - "iopub.status.busy": "2023-08-07T00:19:40.409061Z", - "iopub.status.idle": "2023-08-07T00:19:40.411699Z", - "shell.execute_reply": "2023-08-07T00:19:40.411382Z" + "iopub.execute_input": "2023-08-21T02:30:11.517105Z", + "iopub.status.busy": "2023-08-21T02:30:11.516982Z", + "iopub.status.idle": "2023-08-21T02:30:11.519375Z", + "shell.execute_reply": "2023-08-21T02:30:11.519076Z" }, "lines_to_next_cell": 0 }, @@ -1061,7 +1061,7 @@ }, { "cell_type": "markdown", - "id": "d2dcf543", + "id": "ef559fde", "metadata": {}, "source": [ "The proportion of variance explained by each principal \n", @@ -1071,13 +1071,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "68e47d3a", + "id": "e711d1be", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.413200Z", - "iopub.status.busy": "2023-08-07T00:19:40.413101Z", - "iopub.status.idle": "2023-08-07T00:19:40.415302Z", - "shell.execute_reply": "2023-08-07T00:19:40.415021Z" + "iopub.execute_input": "2023-08-21T02:30:11.520951Z", + "iopub.status.busy": "2023-08-21T02:30:11.520847Z", + "iopub.status.idle": "2023-08-21T02:30:11.523283Z", + "shell.execute_reply": "2023-08-21T02:30:11.523005Z" }, "lines_to_next_cell": 0 }, @@ -1099,7 +1099,7 @@ }, { "cell_type": "markdown", - "id": "831b19b7", + "id": "567f5027", "metadata": {}, "source": [ "We see that the first principal component explains 62.0% of the\n", @@ -1112,13 +1112,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "e87fe198", + "id": "e122eb41", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.416840Z", - "iopub.status.busy": "2023-08-07T00:19:40.416734Z", - "iopub.status.idle": "2023-08-07T00:19:40.552815Z", - "shell.execute_reply": "2023-08-07T00:19:40.552349Z" + "iopub.execute_input": "2023-08-21T02:30:11.524835Z", + "iopub.status.busy": "2023-08-21T02:30:11.524733Z", + "iopub.status.idle": "2023-08-21T02:30:11.658787Z", + "shell.execute_reply": "2023-08-21T02:30:11.658413Z" }, "lines_to_next_cell": 0 }, @@ -1139,7 +1139,7 @@ }, { "cell_type": "markdown", - "id": "2ed96395", + "id": "5de6bff0", "metadata": {}, "source": [ "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure." @@ -1148,13 +1148,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "409fb0c6", + "id": "bef47d90", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.554888Z", - "iopub.status.busy": "2023-08-07T00:19:40.554718Z", - "iopub.status.idle": "2023-08-07T00:19:40.664487Z", - "shell.execute_reply": "2023-08-07T00:19:40.664052Z" + "iopub.execute_input": "2023-08-21T02:30:11.661199Z", + "iopub.status.busy": "2023-08-21T02:30:11.661017Z", + "iopub.status.idle": "2023-08-21T02:30:11.782427Z", + "shell.execute_reply": "2023-08-21T02:30:11.781635Z" }, "lines_to_next_cell": 0 }, @@ -1185,7 +1185,7 @@ }, { "cell_type": "markdown", - "id": "496fb6be", + "id": "5815db80", "metadata": {}, "source": [ "The result is similar to that shown in Figure 12.3. Note\n", @@ -1196,13 +1196,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "e563e41b", + "id": "f3300d9e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.666198Z", - "iopub.status.busy": "2023-08-07T00:19:40.666084Z", - "iopub.status.idle": "2023-08-07T00:19:40.668537Z", - "shell.execute_reply": "2023-08-07T00:19:40.668242Z" + "iopub.execute_input": "2023-08-21T02:30:11.785513Z", + "iopub.status.busy": "2023-08-21T02:30:11.785329Z", + "iopub.status.idle": "2023-08-21T02:30:11.789028Z", + "shell.execute_reply": "2023-08-21T02:30:11.788419Z" }, "lines_to_next_cell": 0 }, @@ -1225,7 +1225,7 @@ }, { "cell_type": "markdown", - "id": "6794b1a3", + "id": "954cd9f7", "metadata": {}, "source": [ "## Matrix Completion\n", @@ -1245,13 +1245,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "f83ad0bc", + "id": "20e6009f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.670153Z", - "iopub.status.busy": "2023-08-07T00:19:40.670010Z", - "iopub.status.idle": "2023-08-07T00:19:40.672783Z", - "shell.execute_reply": "2023-08-07T00:19:40.672475Z" + "iopub.execute_input": "2023-08-21T02:30:11.791569Z", + "iopub.status.busy": "2023-08-21T02:30:11.791444Z", + "iopub.status.idle": "2023-08-21T02:30:11.795478Z", + "shell.execute_reply": "2023-08-21T02:30:11.794972Z" }, "lines_to_next_cell": 0 }, @@ -1275,7 +1275,7 @@ }, { "cell_type": "markdown", - "id": "f9c71e57", + "id": "53e9a370", "metadata": {}, "source": [ "The `np.linalg.svd()` function returns three components, `U`, `D` and `V`. The matrix `V` is equivalent to the\n", @@ -1286,13 +1286,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "cb9bdc46", + "id": "7d9937cf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.674352Z", - "iopub.status.busy": "2023-08-07T00:19:40.674236Z", - "iopub.status.idle": "2023-08-07T00:19:40.676540Z", - "shell.execute_reply": "2023-08-07T00:19:40.676276Z" + "iopub.execute_input": "2023-08-21T02:30:11.797640Z", + "iopub.status.busy": "2023-08-21T02:30:11.797509Z", + "iopub.status.idle": "2023-08-21T02:30:11.800846Z", + "shell.execute_reply": "2023-08-21T02:30:11.800294Z" } }, "outputs": [ @@ -1317,13 +1317,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "f23c101e", + "id": "e58f83a3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.677938Z", - "iopub.status.busy": "2023-08-07T00:19:40.677820Z", - "iopub.status.idle": "2023-08-07T00:19:40.680147Z", - "shell.execute_reply": "2023-08-07T00:19:40.679807Z" + "iopub.execute_input": "2023-08-21T02:30:11.808398Z", + "iopub.status.busy": "2023-08-21T02:30:11.808136Z", + "iopub.status.idle": "2023-08-21T02:30:11.811679Z", + "shell.execute_reply": "2023-08-21T02:30:11.811074Z" }, "lines_to_next_cell": 0 }, @@ -1348,7 +1348,7 @@ }, { "cell_type": "markdown", - "id": "a5d9e0ae", + "id": "22f7625f", "metadata": {}, "source": [ "The matrix `U` corresponds to a *standardized* version of the PCA score matrix (each column standardized to have sum-of-squares one). If we multiply each column of `U` by the corresponding element of `D`, we recover the PCA scores exactly (up to a meaningless sign flip)." @@ -1357,13 +1357,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "4cc49622", + "id": "5c4f9b34", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.681817Z", - "iopub.status.busy": "2023-08-07T00:19:40.681713Z", - "iopub.status.idle": "2023-08-07T00:19:40.684186Z", - "shell.execute_reply": "2023-08-07T00:19:40.683881Z" + "iopub.execute_input": "2023-08-21T02:30:11.813485Z", + "iopub.status.busy": "2023-08-21T02:30:11.813382Z", + "iopub.status.idle": "2023-08-21T02:30:11.816392Z", + "shell.execute_reply": "2023-08-21T02:30:11.815967Z" } }, "outputs": [ @@ -1387,13 +1387,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "c96c9fe1", + "id": "0ce84f1b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.685613Z", - "iopub.status.busy": "2023-08-07T00:19:40.685513Z", - "iopub.status.idle": "2023-08-07T00:19:40.687604Z", - "shell.execute_reply": "2023-08-07T00:19:40.687323Z" + "iopub.execute_input": "2023-08-21T02:30:11.818183Z", + "iopub.status.busy": "2023-08-21T02:30:11.818037Z", + "iopub.status.idle": "2023-08-21T02:30:11.820586Z", + "shell.execute_reply": "2023-08-21T02:30:11.820295Z" }, "lines_to_next_cell": 0 }, @@ -1417,7 +1417,7 @@ }, { "cell_type": "markdown", - "id": "6b7002cb", + "id": "ccc66247", "metadata": {}, "source": [ "While it would be possible to carry out this lab using the `PCA()` estimator,\n", @@ -1432,13 +1432,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "574409d6", + "id": "cd8b4bed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.689075Z", - "iopub.status.busy": "2023-08-07T00:19:40.688979Z", - "iopub.status.idle": "2023-08-07T00:19:40.691538Z", - "shell.execute_reply": "2023-08-07T00:19:40.691269Z" + "iopub.execute_input": "2023-08-21T02:30:11.822223Z", + "iopub.status.busy": "2023-08-21T02:30:11.822124Z", + "iopub.status.idle": "2023-08-21T02:30:11.824710Z", + "shell.execute_reply": "2023-08-21T02:30:11.824451Z" } }, "outputs": [], @@ -1457,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "fd4a5fdf", + "id": "ff75324d", "metadata": {}, "source": [ "Here the array `r_idx`\n", @@ -1472,13 +1472,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "89f190ae", + "id": "7f3bc8f9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.693076Z", - "iopub.status.busy": "2023-08-07T00:19:40.692974Z", - "iopub.status.idle": "2023-08-07T00:19:40.694962Z", - "shell.execute_reply": "2023-08-07T00:19:40.694708Z" + "iopub.execute_input": "2023-08-21T02:30:11.826294Z", + "iopub.status.busy": "2023-08-21T02:30:11.826188Z", + "iopub.status.idle": "2023-08-21T02:30:11.828326Z", + "shell.execute_reply": "2023-08-21T02:30:11.827946Z" }, "lines_to_next_cell": 0 }, @@ -1492,7 +1492,7 @@ }, { "cell_type": "markdown", - "id": "a04129d0", + "id": "ff7e6ca1", "metadata": {}, "source": [ "To conduct Step 1 of the algorithm, we initialize `Xhat` --- this is $\\tilde{\\bf X}$ in Algorithm 12.1 --- by replacing\n", @@ -1505,13 +1505,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "322f339c", + "id": "771a46a7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.696660Z", - "iopub.status.busy": "2023-08-07T00:19:40.696543Z", - "iopub.status.idle": "2023-08-07T00:19:40.698585Z", - "shell.execute_reply": "2023-08-07T00:19:40.698324Z" + "iopub.execute_input": "2023-08-21T02:30:11.830112Z", + "iopub.status.busy": "2023-08-21T02:30:11.829990Z", + "iopub.status.idle": "2023-08-21T02:30:11.832019Z", + "shell.execute_reply": "2023-08-21T02:30:11.831653Z" } }, "outputs": [], @@ -1523,7 +1523,7 @@ }, { "cell_type": "markdown", - "id": "357041e8", + "id": "30a6e972", "metadata": {}, "source": [ "Before we begin Step 2, we set ourselves up to measure the progress of our\n", @@ -1533,13 +1533,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "7e106d1a", + "id": "1416f048", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.700112Z", - "iopub.status.busy": "2023-08-07T00:19:40.700007Z", - "iopub.status.idle": "2023-08-07T00:19:40.702027Z", - "shell.execute_reply": "2023-08-07T00:19:40.701767Z" + "iopub.execute_input": "2023-08-21T02:30:11.834018Z", + "iopub.status.busy": "2023-08-21T02:30:11.833888Z", + "iopub.status.idle": "2023-08-21T02:30:11.836065Z", + "shell.execute_reply": "2023-08-21T02:30:11.835740Z" }, "lines_to_next_cell": 0 }, @@ -1555,7 +1555,7 @@ }, { "cell_type": "markdown", - "id": "105ac73f", + "id": "40dbfa78", "metadata": {}, "source": [ "Here `ismiss` is a logical matrix with the same dimensions as `Xna`;\n", @@ -1573,13 +1573,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "7cb05ce4", + "id": "9eff34aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.703581Z", - "iopub.status.busy": "2023-08-07T00:19:40.703476Z", - "iopub.status.idle": "2023-08-07T00:19:40.706441Z", - "shell.execute_reply": "2023-08-07T00:19:40.706179Z" + "iopub.execute_input": "2023-08-21T02:30:11.838230Z", + "iopub.status.busy": "2023-08-21T02:30:11.838081Z", + "iopub.status.idle": "2023-08-21T02:30:11.841552Z", + "shell.execute_reply": "2023-08-21T02:30:11.841240Z" } }, "outputs": [ @@ -1616,7 +1616,7 @@ }, { "cell_type": "markdown", - "id": "5cd8edf2", + "id": "b005a639", "metadata": {}, "source": [ "We see that after eight iterations, the relative error has fallen below `thresh = 1e-7`, and so the algorithm terminates. When this happens, the mean squared error of the non-missing elements equals 0.381.\n", @@ -1628,13 +1628,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "6f245188", + "id": "7815b948", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.708006Z", - "iopub.status.busy": "2023-08-07T00:19:40.707901Z", - "iopub.status.idle": "2023-08-07T00:19:40.710450Z", - "shell.execute_reply": "2023-08-07T00:19:40.710194Z" + "iopub.execute_input": "2023-08-21T02:30:11.843245Z", + "iopub.status.busy": "2023-08-21T02:30:11.843118Z", + "iopub.status.idle": "2023-08-21T02:30:11.846592Z", + "shell.execute_reply": "2023-08-21T02:30:11.846200Z" }, "lines_to_next_cell": 2 }, @@ -1656,7 +1656,7 @@ }, { "cell_type": "markdown", - "id": "f22444ff", + "id": "9091b3ad", "metadata": {}, "source": [ "In this lab, we implemented Algorithm 12.1 ourselves for didactic purposes. However, a reader who wishes to apply matrix completion to their data might look to more specialized `Python` implementations." @@ -1664,7 +1664,7 @@ }, { "cell_type": "markdown", - "id": "7da441a1", + "id": "e528e2a6", "metadata": {}, "source": [ "## Clustering" @@ -1672,7 +1672,7 @@ }, { "cell_type": "markdown", - "id": "ef4903d6", + "id": "8ba3f642", "metadata": {}, "source": [ "### $K$-Means Clustering\n", @@ -1686,13 +1686,13 @@ { "cell_type": "code", "execution_count": 32, - "id": "345fb41e", + "id": "f63cf4b8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.712093Z", - "iopub.status.busy": "2023-08-07T00:19:40.711979Z", - "iopub.status.idle": "2023-08-07T00:19:40.713935Z", - "shell.execute_reply": "2023-08-07T00:19:40.713674Z" + "iopub.execute_input": "2023-08-21T02:30:11.848430Z", + "iopub.status.busy": "2023-08-21T02:30:11.848311Z", + "iopub.status.idle": "2023-08-21T02:30:11.850755Z", + "shell.execute_reply": "2023-08-21T02:30:11.850430Z" }, "lines_to_next_cell": 0 }, @@ -1706,7 +1706,7 @@ }, { "cell_type": "markdown", - "id": "6ed3587f", + "id": "4bb54e06", "metadata": {}, "source": [ "We now perform $K$-means clustering with $K=2$." @@ -1715,13 +1715,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "3a8c21a2", + "id": "f973c2d4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:40.715319Z", - "iopub.status.busy": "2023-08-07T00:19:40.715219Z", - "iopub.status.idle": "2023-08-07T00:19:41.035873Z", - "shell.execute_reply": "2023-08-07T00:19:41.035293Z" + "iopub.execute_input": "2023-08-21T02:30:11.852603Z", + "iopub.status.busy": "2023-08-21T02:30:11.852463Z", + "iopub.status.idle": "2023-08-21T02:30:12.419856Z", + "shell.execute_reply": "2023-08-21T02:30:12.416814Z" }, "lines_to_next_cell": 0 }, @@ -1734,7 +1734,7 @@ }, { "cell_type": "markdown", - "id": "df4fb0eb", + "id": "eeca2ae3", "metadata": {}, "source": [ "We specify `random_state` to make the results reproducible. The cluster assignments of the 50 observations are contained in `kmeans.labels_`." @@ -1743,13 +1743,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "e3e35b5d", + "id": "e980954b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.038578Z", - "iopub.status.busy": "2023-08-07T00:19:41.038393Z", - "iopub.status.idle": "2023-08-07T00:19:41.041820Z", - "shell.execute_reply": "2023-08-07T00:19:41.041365Z" + "iopub.execute_input": "2023-08-21T02:30:12.436151Z", + "iopub.status.busy": "2023-08-21T02:30:12.434738Z", + "iopub.status.idle": "2023-08-21T02:30:12.484691Z", + "shell.execute_reply": "2023-08-21T02:30:12.459324Z" }, "lines_to_next_cell": 0 }, @@ -1773,7 +1773,7 @@ }, { "cell_type": "markdown", - "id": "6632fa4c", + "id": "d796ee94", "metadata": {}, "source": [ "The $K$-means clustering perfectly separated the observations into two\n", @@ -1785,13 +1785,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "d928650a", + "id": "a94d452c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.043940Z", - "iopub.status.busy": "2023-08-07T00:19:41.043807Z", - "iopub.status.idle": "2023-08-07T00:19:41.152788Z", - "shell.execute_reply": "2023-08-07T00:19:41.152394Z" + "iopub.execute_input": "2023-08-21T02:30:12.510741Z", + "iopub.status.busy": "2023-08-21T02:30:12.510582Z", + "iopub.status.idle": "2023-08-21T02:30:12.636062Z", + "shell.execute_reply": "2023-08-21T02:30:12.635730Z" } }, "outputs": [ @@ -1814,7 +1814,7 @@ }, { "cell_type": "markdown", - "id": "1f6a6d01", + "id": "6463d546", "metadata": {}, "source": [ "Here the observations can be easily plotted because they are\n", @@ -1832,13 +1832,13 @@ { "cell_type": "code", "execution_count": 36, - "id": "92e5175c", + "id": "94ff654c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.154810Z", - "iopub.status.busy": "2023-08-07T00:19:41.154695Z", - "iopub.status.idle": "2023-08-07T00:19:41.278338Z", - "shell.execute_reply": "2023-08-07T00:19:41.277867Z" + "iopub.execute_input": "2023-08-21T02:30:12.638065Z", + "iopub.status.busy": "2023-08-21T02:30:12.637921Z", + "iopub.status.idle": "2023-08-21T02:30:12.806481Z", + "shell.execute_reply": "2023-08-21T02:30:12.805717Z" }, "lines_to_next_cell": 0 }, @@ -1865,7 +1865,7 @@ }, { "cell_type": "markdown", - "id": "82317a30", + "id": "52dceb84", "metadata": {}, "source": [ "When $K=3$, $K$-means clustering splits up the two clusters.\n", @@ -1881,13 +1881,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "4911ecc7", + "id": "b3561317", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.280637Z", - "iopub.status.busy": "2023-08-07T00:19:41.280494Z", - "iopub.status.idle": "2023-08-07T00:19:41.296485Z", - "shell.execute_reply": "2023-08-07T00:19:41.296101Z" + "iopub.execute_input": "2023-08-21T02:30:12.808756Z", + "iopub.status.busy": "2023-08-21T02:30:12.808647Z", + "iopub.status.idle": "2023-08-21T02:30:12.868305Z", + "shell.execute_reply": "2023-08-21T02:30:12.867287Z" }, "lines_to_next_cell": 0 }, @@ -1915,7 +1915,7 @@ }, { "cell_type": "markdown", - "id": "96e6df1f", + "id": "e32af47d", "metadata": {}, "source": [ "Note that `kmeans.inertia_` is the total within-cluster sum\n", @@ -1935,7 +1935,7 @@ }, { "cell_type": "markdown", - "id": "904ea858", + "id": "a34ddf89", "metadata": {}, "source": [ "### Hierarchical Clustering\n", @@ -1954,13 +1954,13 @@ { "cell_type": "code", "execution_count": 38, - "id": "1b42a700", + "id": "be9e4f9c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.298559Z", - "iopub.status.busy": "2023-08-07T00:19:41.298421Z", - "iopub.status.idle": "2023-08-07T00:19:41.304986Z", - "shell.execute_reply": "2023-08-07T00:19:41.304678Z" + "iopub.execute_input": "2023-08-21T02:30:12.871403Z", + "iopub.status.busy": "2023-08-21T02:30:12.871255Z", + "iopub.status.idle": "2023-08-21T02:30:12.877754Z", + "shell.execute_reply": "2023-08-21T02:30:12.877453Z" } }, "outputs": [ @@ -1991,7 +1991,7 @@ }, { "cell_type": "markdown", - "id": "22199f4d", + "id": "efa0aecd", "metadata": {}, "source": [ "This computes the entire dendrogram.\n", @@ -2001,13 +2001,13 @@ { "cell_type": "code", "execution_count": 39, - "id": "50ef7eea", + "id": "f80d8563", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.306962Z", - "iopub.status.busy": "2023-08-07T00:19:41.306831Z", - "iopub.status.idle": "2023-08-07T00:19:41.310423Z", - "shell.execute_reply": "2023-08-07T00:19:41.310047Z" + "iopub.execute_input": "2023-08-21T02:30:12.879743Z", + "iopub.status.busy": "2023-08-21T02:30:12.879627Z", + "iopub.status.idle": "2023-08-21T02:30:12.883455Z", + "shell.execute_reply": "2023-08-21T02:30:12.883063Z" } }, "outputs": [], @@ -2024,7 +2024,7 @@ }, { "cell_type": "markdown", - "id": "7e8db256", + "id": "d51b36f5", "metadata": {}, "source": [ "To use a precomputed distance matrix, we provide an additional\n", @@ -2034,13 +2034,13 @@ { "cell_type": "code", "execution_count": 40, - "id": "bf7a2408", + "id": "83e7ccf8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.312244Z", - "iopub.status.busy": "2023-08-07T00:19:41.312117Z", - "iopub.status.idle": "2023-08-07T00:19:41.317488Z", - "shell.execute_reply": "2023-08-07T00:19:41.317168Z" + "iopub.execute_input": "2023-08-21T02:30:12.885091Z", + "iopub.status.busy": "2023-08-21T02:30:12.884985Z", + "iopub.status.idle": "2023-08-21T02:30:12.890499Z", + "shell.execute_reply": "2023-08-21T02:30:12.890138Z" } }, "outputs": [ @@ -2075,7 +2075,7 @@ }, { "cell_type": "markdown", - "id": "8cd7fb7c", + "id": "d2bfc639", "metadata": {}, "source": [ "We use\n", @@ -2094,13 +2094,13 @@ { "cell_type": "code", "execution_count": 41, - "id": "a118c0ab", + "id": "56ee8cbf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.319281Z", - "iopub.status.busy": "2023-08-07T00:19:41.319164Z", - "iopub.status.idle": "2023-08-07T00:19:41.547955Z", - "shell.execute_reply": "2023-08-07T00:19:41.547603Z" + "iopub.execute_input": "2023-08-21T02:30:12.892483Z", + "iopub.status.busy": "2023-08-21T02:30:12.892271Z", + "iopub.status.idle": "2023-08-21T02:30:13.132407Z", + "shell.execute_reply": "2023-08-21T02:30:13.132103Z" } }, "outputs": [ @@ -2127,7 +2127,7 @@ }, { "cell_type": "markdown", - "id": "a325a001", + "id": "03438b18", "metadata": {}, "source": [ "We may want to color branches of the tree above\n", @@ -2139,13 +2139,13 @@ { "cell_type": "code", "execution_count": 42, - "id": "b1ff41c0", + "id": "10f4fc97", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.549650Z", - "iopub.status.busy": "2023-08-07T00:19:41.549522Z", - "iopub.status.idle": "2023-08-07T00:19:41.777366Z", - "shell.execute_reply": "2023-08-07T00:19:41.776967Z" + "iopub.execute_input": "2023-08-21T02:30:13.134084Z", + "iopub.status.busy": "2023-08-21T02:30:13.133962Z", + "iopub.status.idle": "2023-08-21T02:30:13.363909Z", + "shell.execute_reply": "2023-08-21T02:30:13.363594Z" } }, "outputs": [ @@ -2170,7 +2170,7 @@ }, { "cell_type": "markdown", - "id": "e774708c", + "id": "9b64a78f", "metadata": {}, "source": [ "To determine the cluster labels for each observation associated with a\n", @@ -2181,13 +2181,13 @@ { "cell_type": "code", "execution_count": 43, - "id": "c2752a96", + "id": "3aed342a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.779450Z", - "iopub.status.busy": "2023-08-07T00:19:41.779300Z", - "iopub.status.idle": "2023-08-07T00:19:41.783422Z", - "shell.execute_reply": "2023-08-07T00:19:41.783033Z" + "iopub.execute_input": "2023-08-21T02:30:13.365591Z", + "iopub.status.busy": "2023-08-21T02:30:13.365487Z", + "iopub.status.idle": "2023-08-21T02:30:13.368978Z", + "shell.execute_reply": "2023-08-21T02:30:13.368698Z" } }, "outputs": [ @@ -2210,7 +2210,7 @@ }, { "cell_type": "markdown", - "id": "d501d220", + "id": "0b986d91", "metadata": {}, "source": [ "This can also be achieved by providing an argument `n_clusters`\n", @@ -2223,13 +2223,13 @@ { "cell_type": "code", "execution_count": 44, - "id": "1407f7a4", + "id": "49c6db0c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.785112Z", - "iopub.status.busy": "2023-08-07T00:19:41.785008Z", - "iopub.status.idle": "2023-08-07T00:19:41.788913Z", - "shell.execute_reply": "2023-08-07T00:19:41.788641Z" + "iopub.execute_input": "2023-08-21T02:30:13.370508Z", + "iopub.status.busy": "2023-08-21T02:30:13.370414Z", + "iopub.status.idle": "2023-08-21T02:30:13.373767Z", + "shell.execute_reply": "2023-08-21T02:30:13.373373Z" } }, "outputs": [ @@ -2299,7 +2299,7 @@ }, { "cell_type": "markdown", - "id": "469bbad6", + "id": "8ff90643", "metadata": {}, "source": [ "To scale the variables before performing hierarchical clustering of\n", @@ -2309,13 +2309,13 @@ { "cell_type": "code", "execution_count": 45, - "id": "2d74f224", + "id": "0ef4b7ec", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:41.790458Z", - "iopub.status.busy": "2023-08-07T00:19:41.790357Z", - "iopub.status.idle": "2023-08-07T00:19:42.031301Z", - "shell.execute_reply": "2023-08-07T00:19:42.030904Z" + "iopub.execute_input": "2023-08-21T02:30:13.375598Z", + "iopub.status.busy": "2023-08-21T02:30:13.375476Z", + "iopub.status.idle": "2023-08-21T02:30:13.613632Z", + "shell.execute_reply": "2023-08-21T02:30:13.613228Z" } }, "outputs": [ @@ -2344,7 +2344,7 @@ }, { "cell_type": "markdown", - "id": "3caaa1a5", + "id": "671f8aaf", "metadata": {}, "source": [ "Correlation-based distances between observations can be used for\n", @@ -2364,13 +2364,13 @@ { "cell_type": "code", "execution_count": 46, - "id": "b7f7da12", + "id": "51761ef3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:42.033202Z", - "iopub.status.busy": "2023-08-07T00:19:42.033063Z", - "iopub.status.idle": "2023-08-07T00:19:42.217388Z", - "shell.execute_reply": "2023-08-07T00:19:42.217021Z" + "iopub.execute_input": "2023-08-21T02:30:13.616266Z", + "iopub.status.busy": "2023-08-21T02:30:13.616114Z", + "iopub.status.idle": "2023-08-21T02:30:13.807244Z", + "shell.execute_reply": "2023-08-21T02:30:13.806904Z" }, "lines_to_next_cell": 2 }, @@ -2402,7 +2402,7 @@ }, { "cell_type": "markdown", - "id": "5b00804e", + "id": "f2f77cb2", "metadata": {}, "source": [ "## NCI60 Data Example\n", @@ -2416,13 +2416,13 @@ { "cell_type": "code", "execution_count": 47, - "id": "b94424fc", + "id": "3dbe7baf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:42.219256Z", - "iopub.status.busy": "2023-08-07T00:19:42.219115Z", - "iopub.status.idle": "2023-08-07T00:19:42.227039Z", - "shell.execute_reply": "2023-08-07T00:19:42.226704Z" + "iopub.execute_input": "2023-08-21T02:30:13.808881Z", + "iopub.status.busy": "2023-08-21T02:30:13.808757Z", + "iopub.status.idle": "2023-08-21T02:30:13.815572Z", + "shell.execute_reply": "2023-08-21T02:30:13.815234Z" } }, "outputs": [], @@ -2434,7 +2434,7 @@ }, { "cell_type": "markdown", - "id": "7313fea6", + "id": "6f03fcd9", "metadata": {}, "source": [ "Each cell line is labeled with a cancer type. We do not make use of\n", @@ -2449,13 +2449,13 @@ { "cell_type": "code", "execution_count": 48, - "id": "cea54566", + "id": "8f4e9db0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:42.228793Z", - "iopub.status.busy": "2023-08-07T00:19:42.228704Z", - "iopub.status.idle": "2023-08-07T00:19:42.231034Z", - "shell.execute_reply": "2023-08-07T00:19:42.230727Z" + "iopub.execute_input": "2023-08-21T02:30:13.817455Z", + "iopub.status.busy": "2023-08-21T02:30:13.817327Z", + "iopub.status.idle": "2023-08-21T02:30:13.819934Z", + "shell.execute_reply": "2023-08-21T02:30:13.819588Z" }, "lines_to_next_cell": 2 }, @@ -2477,7 +2477,7 @@ }, { "cell_type": "markdown", - "id": "6ebf27f1", + "id": "7fad9a75", "metadata": {}, "source": [ "We begin by examining the cancer types for the cell lines.\n" @@ -2486,13 +2486,13 @@ { "cell_type": "code", "execution_count": 49, - "id": "4dac41bb", + "id": "6373db4d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:42.232626Z", - "iopub.status.busy": "2023-08-07T00:19:42.232514Z", - "iopub.status.idle": "2023-08-07T00:19:42.236205Z", - "shell.execute_reply": "2023-08-07T00:19:42.235893Z" + "iopub.execute_input": "2023-08-21T02:30:13.821622Z", + "iopub.status.busy": "2023-08-21T02:30:13.821498Z", + "iopub.status.idle": "2023-08-21T02:30:13.825550Z", + "shell.execute_reply": "2023-08-21T02:30:13.825258Z" }, "lines_to_next_cell": 2 }, @@ -2529,7 +2529,7 @@ }, { "cell_type": "markdown", - "id": "8f16f96a", + "id": "c75ae01f", "metadata": {}, "source": [ "### PCA on the NCI60 Data\n", @@ -2542,13 +2542,13 @@ { "cell_type": "code", "execution_count": 50, - "id": "d8ebadd6", + "id": "9f185f83", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:42.237776Z", - "iopub.status.busy": "2023-08-07T00:19:42.237686Z", - "iopub.status.idle": "2023-08-07T00:19:42.764811Z", - "shell.execute_reply": "2023-08-07T00:19:42.760096Z" + "iopub.execute_input": "2023-08-21T02:30:13.827046Z", + "iopub.status.busy": "2023-08-21T02:30:13.826963Z", + "iopub.status.idle": "2023-08-21T02:30:15.128422Z", + "shell.execute_reply": "2023-08-21T02:30:15.127267Z" } }, "outputs": [], @@ -2561,7 +2561,7 @@ }, { "cell_type": "markdown", - "id": "8a8c9932", + "id": "e859fa7c", "metadata": {}, "source": [ "We now plot the first few principal component score vectors, in order\n", @@ -2574,13 +2574,13 @@ { "cell_type": "code", "execution_count": 51, - "id": "63b5efe3", + "id": "b044b197", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:42.771948Z", - "iopub.status.busy": "2023-08-07T00:19:42.771506Z", - "iopub.status.idle": "2023-08-07T00:19:42.971788Z", - "shell.execute_reply": "2023-08-07T00:19:42.971058Z" + "iopub.execute_input": "2023-08-21T02:30:15.132319Z", + "iopub.status.busy": "2023-08-21T02:30:15.130836Z", + "iopub.status.idle": "2023-08-21T02:30:15.450531Z", + "shell.execute_reply": "2023-08-21T02:30:15.450148Z" }, "lines_to_next_cell": 0 }, @@ -2619,7 +2619,7 @@ }, { "cell_type": "markdown", - "id": "87b33a9c", + "id": "01debda9", "metadata": {}, "source": [ "On the whole, cell lines corresponding to a single cancer type do tend to\n", @@ -2633,7 +2633,7 @@ }, { "cell_type": "markdown", - "id": "890fdb54", + "id": "8695f122", "metadata": {}, "source": [ "We can also plot the percent variance\n", @@ -2644,13 +2644,13 @@ { "cell_type": "code", "execution_count": 52, - "id": "e20c3cc1", + "id": "b2450bb2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:42.976556Z", - "iopub.status.busy": "2023-08-07T00:19:42.975788Z", - "iopub.status.idle": "2023-08-07T00:19:43.134142Z", - "shell.execute_reply": "2023-08-07T00:19:43.133869Z" + "iopub.execute_input": "2023-08-21T02:30:15.453108Z", + "iopub.status.busy": "2023-08-21T02:30:15.452934Z", + "iopub.status.idle": "2023-08-21T02:30:15.641832Z", + "shell.execute_reply": "2023-08-21T02:30:15.641441Z" }, "lines_to_next_cell": 0 }, @@ -2685,7 +2685,7 @@ }, { "cell_type": "markdown", - "id": "cb6eb4f8", + "id": "6c9d8235", "metadata": {}, "source": [ "We see that together, the first seven principal components explain\n", @@ -2702,7 +2702,7 @@ }, { "cell_type": "markdown", - "id": "8962791f", + "id": "a4de7a0d", "metadata": {}, "source": [ "### Clustering the Observations of the NCI60 Data\n", @@ -2717,13 +2717,13 @@ { "cell_type": "code", "execution_count": 53, - "id": "622de805", + "id": "f3f85512", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:43.135947Z", - "iopub.status.busy": "2023-08-07T00:19:43.135742Z", - "iopub.status.idle": "2023-08-07T00:19:43.138541Z", - "shell.execute_reply": "2023-08-07T00:19:43.138181Z" + "iopub.execute_input": "2023-08-21T02:30:15.645761Z", + "iopub.status.busy": "2023-08-21T02:30:15.645577Z", + "iopub.status.idle": "2023-08-21T02:30:15.648982Z", + "shell.execute_reply": "2023-08-21T02:30:15.648549Z" } }, "outputs": [], @@ -2746,7 +2746,7 @@ }, { "cell_type": "markdown", - "id": "9cf5f836", + "id": "3f24eed8", "metadata": {}, "source": [ "Let’s plot our results." @@ -2755,13 +2755,13 @@ { "cell_type": "code", "execution_count": 54, - "id": "54d40449", + "id": "5cbeeb19", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:43.140415Z", - "iopub.status.busy": "2023-08-07T00:19:43.140161Z", - "iopub.status.idle": "2023-08-07T00:19:44.449125Z", - "shell.execute_reply": "2023-08-07T00:19:44.448816Z" + "iopub.execute_input": "2023-08-21T02:30:15.651172Z", + "iopub.status.busy": "2023-08-21T02:30:15.651062Z", + "iopub.status.idle": "2023-08-21T02:30:17.138064Z", + "shell.execute_reply": "2023-08-21T02:30:17.137456Z" }, "lines_to_next_cell": 0 }, @@ -2786,7 +2786,7 @@ }, { "cell_type": "markdown", - "id": "1703afbb", + "id": "91af9aad", "metadata": {}, "source": [ "We see that the\n", @@ -2800,7 +2800,7 @@ "cluster together, although the clustering is not perfect. We will use\n", "complete linkage hierarchical clustering for the analysis that\n", "follows.\n", - "\n", + " \n", "We can cut the dendrogram at the height that will yield a particular\n", "number of clusters, say four:" ] @@ -2808,13 +2808,13 @@ { "cell_type": "code", "execution_count": 55, - "id": "dc80afc8", + "id": "1eb3c92e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:44.450952Z", - "iopub.status.busy": "2023-08-07T00:19:44.450828Z", - "iopub.status.idle": "2023-08-07T00:19:44.462134Z", - "shell.execute_reply": "2023-08-07T00:19:44.461872Z" + "iopub.execute_input": "2023-08-21T02:30:17.141105Z", + "iopub.status.busy": "2023-08-21T02:30:17.140950Z", + "iopub.status.idle": "2023-08-21T02:30:17.153308Z", + "shell.execute_reply": "2023-08-21T02:30:17.152985Z" }, "lines_to_next_cell": 2 }, @@ -2989,7 +2989,7 @@ }, { "cell_type": "markdown", - "id": "d8ecae69", + "id": "194d0034", "metadata": {}, "source": [ "There are some clear patterns. All the leukemia cell lines fall in\n", @@ -3002,13 +3002,13 @@ { "cell_type": "code", "execution_count": 56, - "id": "40ff59f9", + "id": "e3c2841c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:44.463951Z", - "iopub.status.busy": "2023-08-07T00:19:44.463834Z", - "iopub.status.idle": "2023-08-07T00:19:44.884205Z", - "shell.execute_reply": "2023-08-07T00:19:44.883879Z" + "iopub.execute_input": "2023-08-21T02:30:17.155262Z", + "iopub.status.busy": "2023-08-21T02:30:17.155100Z", + "iopub.status.idle": "2023-08-21T02:30:17.595981Z", + "shell.execute_reply": "2023-08-21T02:30:17.595541Z" } }, "outputs": [ @@ -3031,7 +3031,7 @@ }, { "cell_type": "markdown", - "id": "1c03368c", + "id": "f0154576", "metadata": {}, "source": [ "The `axhline()` function draws a horizontal line line on top of any\n", @@ -3051,13 +3051,13 @@ { "cell_type": "code", "execution_count": 57, - "id": "1587e83b", + "id": "94dfe5a0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:44.886050Z", - "iopub.status.busy": "2023-08-07T00:19:44.885932Z", - "iopub.status.idle": "2023-08-07T00:19:46.259058Z", - "shell.execute_reply": "2023-08-07T00:19:46.257441Z" + "iopub.execute_input": "2023-08-21T02:30:17.598183Z", + "iopub.status.busy": "2023-08-21T02:30:17.598049Z", + "iopub.status.idle": "2023-08-21T02:30:46.877046Z", + "shell.execute_reply": "2023-08-21T02:30:46.874804Z" } }, "outputs": [ @@ -3152,7 +3152,7 @@ }, { "cell_type": "markdown", - "id": "b49c4a73", + "id": "54c529ff", "metadata": {}, "source": [ "We see that the four clusters obtained using hierarchical clustering\n", @@ -3176,13 +3176,13 @@ { "cell_type": "code", "execution_count": 58, - "id": "b09ceeab", + "id": "abd51940", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:46.265164Z", - "iopub.status.busy": "2023-08-07T00:19:46.264638Z", - "iopub.status.idle": "2023-08-07T00:19:46.685470Z", - "shell.execute_reply": "2023-08-07T00:19:46.685151Z" + "iopub.execute_input": "2023-08-21T02:30:46.888673Z", + "iopub.status.busy": "2023-08-21T02:30:46.888240Z", + "iopub.status.idle": "2023-08-21T02:30:47.344737Z", + "shell.execute_reply": "2023-08-21T02:30:47.344286Z" }, "lines_to_next_cell": 0 }, @@ -3379,7 +3379,7 @@ }, { "cell_type": "markdown", - "id": "5e1733b5", + "id": "2e388ef2", "metadata": {}, "source": [ "\n", diff --git a/Ch13-multiple-lab.Rmd b/Ch13-multiple-lab.Rmd index ef5db15..bee30c3 100644 --- a/Ch13-multiple-lab.Rmd +++ b/Ch13-multiple-lab.Rmd @@ -121,7 +121,7 @@ amounts to quite a weak signal, and it resulted in a high number of Type II errors. Let’s instead simulate data with a stronger signal, so that the ratio of the mean to the standard deviation for the false null hypotheses equals $1$. We make only 10 Type II errors. - + ```{python} true_mean = np.array([1]*50 + [0]*50) @@ -244,6 +244,7 @@ mult_test(fund_mini_pvals, method = "holm", alpha=0.05)[:2] As discussed previously, Manager One seems to perform particularly well, whereas Manager Two has poor performance. + ```{python} fund_mini.mean() @@ -575,5 +576,5 @@ ax.set_xlabel("Number of Rejections") ax.set_ylabel("False Discovery Rate"); ``` - + diff --git a/Ch13-multiple-lab.ipynb b/Ch13-multiple-lab.ipynb index 65c7fd0..bb9684d 100644 --- a/Ch13-multiple-lab.ipynb +++ b/Ch13-multiple-lab.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "75b2d75c", + "id": "687e9b80", "metadata": {}, "source": [ "\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "34e410a6", + "id": "9f594a41", "metadata": {}, "source": [ "We include our usual imports seen in earlier labs." @@ -23,13 +23,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "1f928b2d", + "id": "7cc4fbeb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:48.489971Z", - "iopub.status.busy": "2023-08-07T00:19:48.489727Z", - "iopub.status.idle": "2023-08-07T00:19:50.216508Z", - "shell.execute_reply": "2023-08-07T00:19:50.215573Z" + "iopub.execute_input": "2023-08-21T02:29:16.417394Z", + "iopub.status.busy": "2023-08-21T02:29:16.417287Z", + "iopub.status.idle": "2023-08-21T02:29:17.613483Z", + "shell.execute_reply": "2023-08-21T02:29:17.613156Z" } }, "outputs": [], @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "12319e0a", + "id": "08ba7bed", "metadata": {}, "source": [ "We also collect the new imports\n", @@ -53,13 +53,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "eb4b32aa", + "id": "595efc18", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.219989Z", - "iopub.status.busy": "2023-08-07T00:19:50.219429Z", - "iopub.status.idle": "2023-08-07T00:19:50.223239Z", - "shell.execute_reply": "2023-08-07T00:19:50.222392Z" + "iopub.execute_input": "2023-08-21T02:29:17.615551Z", + "iopub.status.busy": "2023-08-21T02:29:17.615375Z", + "iopub.status.idle": "2023-08-21T02:29:17.617379Z", + "shell.execute_reply": "2023-08-21T02:29:17.617087Z" }, "lines_to_next_cell": 2 }, @@ -78,7 +78,7 @@ }, { "cell_type": "markdown", - "id": "a2747e58", + "id": "69e5023e", "metadata": {}, "source": [ "## Review of Hypothesis Tests\n", @@ -92,13 +92,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "e12ac0cd", + "id": "985d1d6e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.225454Z", - "iopub.status.busy": "2023-08-07T00:19:50.225335Z", - "iopub.status.idle": "2023-08-07T00:19:50.228651Z", - "shell.execute_reply": "2023-08-07T00:19:50.228301Z" + "iopub.execute_input": "2023-08-21T02:29:17.618995Z", + "iopub.status.busy": "2023-08-21T02:29:17.618887Z", + "iopub.status.idle": "2023-08-21T02:29:17.620921Z", + "shell.execute_reply": "2023-08-21T02:29:17.620629Z" } }, "outputs": [], @@ -111,7 +111,7 @@ }, { "cell_type": "markdown", - "id": "70d37233", + "id": "9ae561c4", "metadata": {}, "source": [ "To begin, we use `ttest_1samp()` from the\n", @@ -122,13 +122,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "04d0f49e", + "id": "753d612a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.230854Z", - "iopub.status.busy": "2023-08-07T00:19:50.230727Z", - "iopub.status.idle": "2023-08-07T00:19:50.236745Z", - "shell.execute_reply": "2023-08-07T00:19:50.236388Z" + "iopub.execute_input": "2023-08-21T02:29:17.622537Z", + "iopub.status.busy": "2023-08-21T02:29:17.622429Z", + "iopub.status.idle": "2023-08-21T02:29:17.626063Z", + "shell.execute_reply": "2023-08-21T02:29:17.625801Z" } }, "outputs": [ @@ -150,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "cf83426f", + "id": "5d9dc17f", "metadata": {}, "source": [ "The $p$-value comes out to 0.931, which is not low enough to\n", @@ -169,13 +169,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "d1f0c695", + "id": "facd6569", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.238822Z", - "iopub.status.busy": "2023-08-07T00:19:50.238685Z", - "iopub.status.idle": "2023-08-07T00:19:50.263876Z", - "shell.execute_reply": "2023-08-07T00:19:50.263508Z" + "iopub.execute_input": "2023-08-21T02:29:17.627714Z", + "iopub.status.busy": "2023-08-21T02:29:17.627617Z", + "iopub.status.idle": "2023-08-21T02:29:17.651726Z", + "shell.execute_reply": "2023-08-21T02:29:17.651448Z" }, "lines_to_next_cell": 0 }, @@ -195,7 +195,7 @@ }, { "cell_type": "markdown", - "id": "3d8e0d96", + "id": "4094daa7", "metadata": {}, "source": [ "Since this is a simulated data set, we can create a $2 \\times 2$ table\n", @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "7a9594a0", + "id": "e89ef3eb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.266708Z", - "iopub.status.busy": "2023-08-07T00:19:50.266387Z", - "iopub.status.idle": "2023-08-07T00:19:50.280831Z", - "shell.execute_reply": "2023-08-07T00:19:50.280194Z" + "iopub.execute_input": "2023-08-21T02:29:17.653344Z", + "iopub.status.busy": "2023-08-21T02:29:17.653256Z", + "iopub.status.idle": "2023-08-21T02:29:17.662644Z", + "shell.execute_reply": "2023-08-21T02:29:17.662346Z" }, "lines_to_next_cell": 0 }, @@ -282,7 +282,7 @@ }, { "cell_type": "markdown", - "id": "9610c817", + "id": "a10ba423", "metadata": {}, "source": [ "Therefore, at level $\\alpha=0.05$, we reject 15 of the 50 false\n", @@ -299,19 +299,20 @@ "amounts to quite a weak signal, and it resulted in a high number of\n", "Type II errors. Let’s instead simulate data with a stronger signal,\n", "so that the ratio of the mean to the standard deviation for the false\n", - "null hypotheses equals $1$. We make only 10 Type II errors.\n" + "null hypotheses equals $1$. We make only 10 Type II errors.\n", + " " ] }, { "cell_type": "code", "execution_count": 7, - "id": "25f7fc5d", + "id": "ae184aaf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.283336Z", - "iopub.status.busy": "2023-08-07T00:19:50.283198Z", - "iopub.status.idle": "2023-08-07T00:19:50.317664Z", - "shell.execute_reply": "2023-08-07T00:19:50.317356Z" + "iopub.execute_input": "2023-08-21T02:29:17.664327Z", + "iopub.status.busy": "2023-08-21T02:29:17.664213Z", + "iopub.status.idle": "2023-08-21T02:29:17.690928Z", + "shell.execute_reply": "2023-08-21T02:29:17.690657Z" }, "lines_to_next_cell": 0 }, @@ -394,7 +395,7 @@ }, { "cell_type": "markdown", - "id": "bb70c597", + "id": "7ca15d3f", "metadata": {}, "source": [ " " @@ -402,7 +403,7 @@ }, { "cell_type": "markdown", - "id": "f6953d33", + "id": "9e9f5977", "metadata": {}, "source": [ "## Family-Wise Error Rate\n", @@ -417,13 +418,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "369b5bd3", + "id": "0295fe68", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.319399Z", - "iopub.status.busy": "2023-08-07T00:19:50.319268Z", - "iopub.status.idle": "2023-08-07T00:19:50.674219Z", - "shell.execute_reply": "2023-08-07T00:19:50.673887Z" + "iopub.execute_input": "2023-08-21T02:29:17.692568Z", + "iopub.status.busy": "2023-08-21T02:29:17.692459Z", + "iopub.status.idle": "2023-08-21T02:29:17.899403Z", + "shell.execute_reply": "2023-08-21T02:29:17.899081Z" } }, "outputs": [ @@ -454,7 +455,7 @@ }, { "cell_type": "markdown", - "id": "3a81479e", + "id": "fecaca9e", "metadata": {}, "source": [ "As discussed previously, even for moderate values of $m$ such as $50$,\n", @@ -473,13 +474,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "9ce7a19f", + "id": "406e59a8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.676029Z", - "iopub.status.busy": "2023-08-07T00:19:50.675909Z", - "iopub.status.idle": "2023-08-07T00:19:50.715451Z", - "shell.execute_reply": "2023-08-07T00:19:50.715145Z" + "iopub.execute_input": "2023-08-21T02:29:17.901146Z", + "iopub.status.busy": "2023-08-21T02:29:17.901041Z", + "iopub.status.idle": "2023-08-21T02:29:17.939312Z", + "shell.execute_reply": "2023-08-21T02:29:17.939019Z" } }, "outputs": [ @@ -505,7 +506,7 @@ }, { "cell_type": "markdown", - "id": "7561e3a3", + "id": "87bab88b", "metadata": {}, "source": [ "The $p$-values are low for Managers One and Three, and high for the\n", @@ -530,7 +531,7 @@ }, { "cell_type": "markdown", - "id": "5b608e46", + "id": "d0c7a2a0", "metadata": {}, "source": [ "The `mult_test()` function takes $p$-values and a `method` argument, as well as an optional\n", @@ -541,13 +542,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "de6cffed", + "id": "d4f6a247", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.717476Z", - "iopub.status.busy": "2023-08-07T00:19:50.717258Z", - "iopub.status.idle": "2023-08-07T00:19:50.719841Z", - "shell.execute_reply": "2023-08-07T00:19:50.719577Z" + "iopub.execute_input": "2023-08-21T02:29:17.941032Z", + "iopub.status.busy": "2023-08-21T02:29:17.940919Z", + "iopub.status.idle": "2023-08-21T02:29:17.943369Z", + "shell.execute_reply": "2023-08-21T02:29:17.943081Z" }, "lines_to_next_cell": 2 }, @@ -570,7 +571,7 @@ }, { "cell_type": "markdown", - "id": "5135c6b9", + "id": "4d5bc7e7", "metadata": {}, "source": [ "The $p$-values `bonf` are simply the `fund_mini_pvalues` multiplied by 5 and truncated to be less than\n", @@ -580,13 +581,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "0de71500", + "id": "01a29d71", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.721450Z", - "iopub.status.busy": "2023-08-07T00:19:50.721342Z", - "iopub.status.idle": "2023-08-07T00:19:50.723962Z", - "shell.execute_reply": "2023-08-07T00:19:50.723691Z" + "iopub.execute_input": "2023-08-21T02:29:17.944859Z", + "iopub.status.busy": "2023-08-21T02:29:17.944760Z", + "iopub.status.idle": "2023-08-21T02:29:17.946888Z", + "shell.execute_reply": "2023-08-21T02:29:17.946639Z" } }, "outputs": [ @@ -608,7 +609,7 @@ }, { "cell_type": "markdown", - "id": "1f0bc112", + "id": "e68c9051", "metadata": {}, "source": [ "Therefore, using Bonferroni’s method, we are able to reject the null hypothesis only for Manager\n", @@ -622,13 +623,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "f7e87bdb", + "id": "95454eb4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.725450Z", - "iopub.status.busy": "2023-08-07T00:19:50.725349Z", - "iopub.status.idle": "2023-08-07T00:19:50.777055Z", - "shell.execute_reply": "2023-08-07T00:19:50.776612Z" + "iopub.execute_input": "2023-08-21T02:29:17.948474Z", + "iopub.status.busy": "2023-08-21T02:29:17.948372Z", + "iopub.status.idle": "2023-08-21T02:29:17.990740Z", + "shell.execute_reply": "2023-08-21T02:29:17.990464Z" }, "lines_to_next_cell": 2 }, @@ -651,23 +652,24 @@ }, { "cell_type": "markdown", - "id": "f762fecd", + "id": "587b5b48", "metadata": {}, "source": [ "As discussed previously, Manager One seems to perform particularly\n", - "well, whereas Manager Two has poor performance." + "well, whereas Manager Two has poor performance.\n", + " " ] }, { "cell_type": "code", "execution_count": 13, - "id": "e88be376", + "id": "1f1ac764", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.779763Z", - "iopub.status.busy": "2023-08-07T00:19:50.779554Z", - "iopub.status.idle": "2023-08-07T00:19:50.783798Z", - "shell.execute_reply": "2023-08-07T00:19:50.783418Z" + "iopub.execute_input": "2023-08-21T02:29:17.992261Z", + "iopub.status.busy": "2023-08-21T02:29:17.992149Z", + "iopub.status.idle": "2023-08-21T02:29:17.995141Z", + "shell.execute_reply": "2023-08-21T02:29:17.994894Z" }, "lines_to_next_cell": 2 }, @@ -694,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "88dbf0a6", + "id": "e3c4b716", "metadata": {}, "source": [ "Is there evidence of a meaningful difference in performance between\n", @@ -705,13 +707,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "41149af6", + "id": "298d975d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.786752Z", - "iopub.status.busy": "2023-08-07T00:19:50.786580Z", - "iopub.status.idle": "2023-08-07T00:19:50.791095Z", - "shell.execute_reply": "2023-08-07T00:19:50.790607Z" + "iopub.execute_input": "2023-08-21T02:29:17.996686Z", + "iopub.status.busy": "2023-08-21T02:29:17.996590Z", + "iopub.status.idle": "2023-08-21T02:29:17.999332Z", + "shell.execute_reply": "2023-08-21T02:29:17.999076Z" } }, "outputs": [ @@ -733,7 +735,7 @@ }, { "cell_type": "markdown", - "id": "1aca6122", + "id": "3908d7d2", "metadata": {}, "source": [ "The test results in a $p$-value of 0.038,\n", @@ -757,13 +759,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "61aabda7", + "id": "be117713", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:50.793577Z", - "iopub.status.busy": "2023-08-07T00:19:50.793394Z", - "iopub.status.idle": "2023-08-07T00:19:51.318999Z", - "shell.execute_reply": "2023-08-07T00:19:51.318071Z" + "iopub.execute_input": "2023-08-21T02:29:18.000853Z", + "iopub.status.busy": "2023-08-21T02:29:18.000747Z", + "iopub.status.idle": "2023-08-21T02:29:18.487357Z", + "shell.execute_reply": "2023-08-21T02:29:18.487078Z" }, "lines_to_next_cell": 2 }, @@ -799,7 +801,7 @@ }, { "cell_type": "markdown", - "id": "e0084fc5", + "id": "0fdf963f", "metadata": {}, "source": [ "The `pairwise_tukeyhsd()` function provides confidence intervals\n", @@ -817,13 +819,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "cbcad4de", + "id": "537c4ea8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:51.321200Z", - "iopub.status.busy": "2023-08-07T00:19:51.321046Z", - "iopub.status.idle": "2023-08-07T00:19:51.411142Z", - "shell.execute_reply": "2023-08-07T00:19:51.410682Z" + "iopub.execute_input": "2023-08-21T02:29:18.489069Z", + "iopub.status.busy": "2023-08-21T02:29:18.488949Z", + "iopub.status.idle": "2023-08-21T02:29:18.570869Z", + "shell.execute_reply": "2023-08-21T02:29:18.570427Z" } }, "outputs": [ @@ -845,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "6278d13c", + "id": "8590f246", "metadata": {}, "source": [ "## False Discovery Rate\n", @@ -858,13 +860,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "b5842190", + "id": "2c88ec87", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:51.413331Z", - "iopub.status.busy": "2023-08-07T00:19:51.413176Z", - "iopub.status.idle": "2023-08-07T00:19:51.848427Z", - "shell.execute_reply": "2023-08-07T00:19:51.847956Z" + "iopub.execute_input": "2023-08-21T02:29:18.572454Z", + "iopub.status.busy": "2023-08-21T02:29:18.572341Z", + "iopub.status.idle": "2023-08-21T02:29:19.005707Z", + "shell.execute_reply": "2023-08-21T02:29:19.005387Z" } }, "outputs": [], @@ -876,7 +878,7 @@ }, { "cell_type": "markdown", - "id": "80fc2fcc", + "id": "80e77fab", "metadata": {}, "source": [ "There are far too many managers to consider trying to control the FWER.\n", @@ -887,13 +889,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "7c9d8bed", + "id": "b6d56819", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:51.850663Z", - "iopub.status.busy": "2023-08-07T00:19:51.850523Z", - "iopub.status.idle": "2023-08-07T00:19:51.854777Z", - "shell.execute_reply": "2023-08-07T00:19:51.854196Z" + "iopub.execute_input": "2023-08-21T02:29:19.007847Z", + "iopub.status.busy": "2023-08-21T02:29:19.007564Z", + "iopub.status.idle": "2023-08-21T02:29:19.010742Z", + "shell.execute_reply": "2023-08-21T02:29:19.010371Z" } }, "outputs": [ @@ -916,7 +918,7 @@ }, { "cell_type": "markdown", - "id": "4f73096d", + "id": "b4662444", "metadata": {}, "source": [ "The *q-values* output by the\n", @@ -932,13 +934,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "bfa39f7c", + "id": "b00da3a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:51.856795Z", - "iopub.status.busy": "2023-08-07T00:19:51.856678Z", - "iopub.status.idle": "2023-08-07T00:19:51.859719Z", - "shell.execute_reply": "2023-08-07T00:19:51.859327Z" + "iopub.execute_input": "2023-08-21T02:29:19.012400Z", + "iopub.status.busy": "2023-08-21T02:29:19.012298Z", + "iopub.status.idle": "2023-08-21T02:29:19.015314Z", + "shell.execute_reply": "2023-08-21T02:29:19.014978Z" }, "lines_to_next_cell": 0 }, @@ -960,7 +962,7 @@ }, { "cell_type": "markdown", - "id": "ccb44c8d", + "id": "fdccb808", "metadata": {}, "source": [ "We find that 146 of the 2,000 fund managers have a $q$-value below\n", @@ -976,13 +978,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "70b69b47", + "id": "1c230117", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:51.861924Z", - "iopub.status.busy": "2023-08-07T00:19:51.861522Z", - "iopub.status.idle": "2023-08-07T00:19:51.864394Z", - "shell.execute_reply": "2023-08-07T00:19:51.863987Z" + "iopub.execute_input": "2023-08-21T02:29:19.016857Z", + "iopub.status.busy": "2023-08-21T02:29:19.016769Z", + "iopub.status.idle": "2023-08-21T02:29:19.019332Z", + "shell.execute_reply": "2023-08-21T02:29:19.019032Z" }, "lines_to_next_cell": 2 }, @@ -1004,7 +1006,7 @@ }, { "cell_type": "markdown", - "id": "c8a969f4", + "id": "6112239d", "metadata": {}, "source": [ "Figure 13.6 displays the ordered\n", @@ -1026,13 +1028,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "4c0ddea1", + "id": "62289650", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:51.865978Z", - "iopub.status.busy": "2023-08-07T00:19:51.865869Z", - "iopub.status.idle": "2023-08-07T00:19:51.868792Z", - "shell.execute_reply": "2023-08-07T00:19:51.868357Z" + "iopub.execute_input": "2023-08-21T02:29:19.021112Z", + "iopub.status.busy": "2023-08-21T02:29:19.020904Z", + "iopub.status.idle": "2023-08-21T02:29:19.023622Z", + "shell.execute_reply": "2023-08-21T02:29:19.023338Z" } }, "outputs": [], @@ -1051,7 +1053,7 @@ }, { "cell_type": "markdown", - "id": "ddeb3900", + "id": "c36b13b7", "metadata": {}, "source": [ "We now reproduce the middle panel of Figure 13.6." @@ -1060,13 +1062,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "0314eac9", + "id": "18b3c0ed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:51.871473Z", - "iopub.status.busy": "2023-08-07T00:19:51.871214Z", - "iopub.status.idle": "2023-08-07T00:19:52.126671Z", - "shell.execute_reply": "2023-08-07T00:19:52.126261Z" + "iopub.execute_input": "2023-08-21T02:29:19.025191Z", + "iopub.status.busy": "2023-08-21T02:29:19.025074Z", + "iopub.status.idle": "2023-08-21T02:29:19.262207Z", + "shell.execute_reply": "2023-08-21T02:29:19.261823Z" }, "lines_to_next_cell": 2 }, @@ -1096,7 +1098,7 @@ }, { "cell_type": "markdown", - "id": "83416f4a", + "id": "d87198e4", "metadata": {}, "source": [ "## A Re-Sampling Approach\n", @@ -1110,13 +1112,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "b59b8137", + "id": "eb79e606", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:52.129167Z", - "iopub.status.busy": "2023-08-07T00:19:52.128842Z", - "iopub.status.idle": "2023-08-07T00:19:52.208320Z", - "shell.execute_reply": "2023-08-07T00:19:52.207936Z" + "iopub.execute_input": "2023-08-21T02:29:19.264174Z", + "iopub.status.busy": "2023-08-21T02:29:19.264030Z", + "iopub.status.idle": "2023-08-21T02:29:19.339232Z", + "shell.execute_reply": "2023-08-21T02:29:19.338912Z" }, "lines_to_next_cell": 2 }, @@ -1145,7 +1147,7 @@ }, { "cell_type": "markdown", - "id": "5534c8d4", + "id": "659ee2b8", "metadata": {}, "source": [ "There are four classes of cancer. For each gene, we compare the mean\n", @@ -1161,13 +1163,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "96fb2f61", + "id": "1afbcf47", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:52.210235Z", - "iopub.status.busy": "2023-08-07T00:19:52.210101Z", - "iopub.status.idle": "2023-08-07T00:19:52.215004Z", - "shell.execute_reply": "2023-08-07T00:19:52.214604Z" + "iopub.execute_input": "2023-08-21T02:29:19.341009Z", + "iopub.status.busy": "2023-08-21T02:29:19.340889Z", + "iopub.status.idle": "2023-08-21T02:29:19.344670Z", + "shell.execute_reply": "2023-08-21T02:29:19.344391Z" }, "lines_to_next_cell": 2 }, @@ -1195,7 +1197,7 @@ }, { "cell_type": "markdown", - "id": "3131124e", + "id": "61f24919", "metadata": {}, "source": [ "However, this $p$-value relies on the assumption that under the null\n", @@ -1214,13 +1216,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "fdc229fa", + "id": "f73f4c6d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:52.217206Z", - "iopub.status.busy": "2023-08-07T00:19:52.217085Z", - "iopub.status.idle": "2023-08-07T00:19:54.776066Z", - "shell.execute_reply": "2023-08-07T00:19:54.775642Z" + "iopub.execute_input": "2023-08-21T02:29:19.346368Z", + "iopub.status.busy": "2023-08-21T02:29:19.346227Z", + "iopub.status.idle": "2023-08-21T02:29:21.776569Z", + "shell.execute_reply": "2023-08-21T02:29:21.776267Z" }, "lines_to_next_cell": 2 }, @@ -1253,7 +1255,7 @@ }, { "cell_type": "markdown", - "id": "c7fc4557", + "id": "a97f74af", "metadata": {}, "source": [ "This fraction, 0.0398,\n", @@ -1265,13 +1267,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "e3894695", + "id": "062daf19", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:54.778563Z", - "iopub.status.busy": "2023-08-07T00:19:54.778388Z", - "iopub.status.idle": "2023-08-07T00:19:55.017161Z", - "shell.execute_reply": "2023-08-07T00:19:55.016821Z" + "iopub.execute_input": "2023-08-21T02:29:21.778366Z", + "iopub.status.busy": "2023-08-21T02:29:21.778242Z", + "iopub.status.idle": "2023-08-21T02:29:21.990476Z", + "shell.execute_reply": "2023-08-21T02:29:21.989965Z" }, "lines_to_next_cell": 0 }, @@ -1307,7 +1309,7 @@ }, { "cell_type": "markdown", - "id": "3bd21158", + "id": "e81b939b", "metadata": {}, "source": [ "The re-sampling-based null distribution is almost identical to the theoretical null distribution, which is displayed in red.\n", @@ -1325,13 +1327,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "3b7392cb", + "id": "6d14fcad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:19:55.019036Z", - "iopub.status.busy": "2023-08-07T00:19:55.018920Z", - "iopub.status.idle": "2023-08-07T00:39:19.291005Z", - "shell.execute_reply": "2023-08-07T00:39:19.287314Z" + "iopub.execute_input": "2023-08-21T02:29:21.992665Z", + "iopub.status.busy": "2023-08-21T02:29:21.992515Z", + "iopub.status.idle": "2023-08-21T02:34:05.930300Z", + "shell.execute_reply": "2023-08-21T02:34:05.929181Z" } }, "outputs": [], @@ -1358,7 +1360,7 @@ }, { "cell_type": "markdown", - "id": "1b92df1b", + "id": "06286699", "metadata": {}, "source": [ "Next, we compute the number of rejected null hypotheses $R$, the\n", @@ -1371,13 +1373,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "cac15616", + "id": "8f0ec909", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:39:19.314420Z", - "iopub.status.busy": "2023-08-07T00:39:19.313452Z", - "iopub.status.idle": "2023-08-07T00:39:19.544251Z", - "shell.execute_reply": "2023-08-07T00:39:19.543932Z" + "iopub.execute_input": "2023-08-21T02:34:05.935513Z", + "iopub.status.busy": "2023-08-21T02:34:05.935323Z", + "iopub.status.idle": "2023-08-21T02:34:06.118079Z", + "shell.execute_reply": "2023-08-21T02:34:06.117633Z" } }, "outputs": [], @@ -1394,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "f6779ea0", + "id": "e26b64c6", "metadata": {}, "source": [ "Now, for any given FDR, we can find the genes that will be\n", @@ -1412,13 +1414,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "9661eb10", + "id": "f11339e5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:39:19.546693Z", - "iopub.status.busy": "2023-08-07T00:39:19.546543Z", - "iopub.status.idle": "2023-08-07T00:39:19.549970Z", - "shell.execute_reply": "2023-08-07T00:39:19.549697Z" + "iopub.execute_input": "2023-08-21T02:34:06.120138Z", + "iopub.status.busy": "2023-08-21T02:34:06.119994Z", + "iopub.status.idle": "2023-08-21T02:34:06.123846Z", + "shell.execute_reply": "2023-08-21T02:34:06.123478Z" } }, "outputs": [ @@ -1457,7 +1459,7 @@ }, { "cell_type": "markdown", - "id": "001e3fc1", + "id": "e145621b", "metadata": {}, "source": [ "At an FDR threshold of 0.2, more genes are selected, at the cost of having a higher expected\n", @@ -1467,13 +1469,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "18ad4900", + "id": "d2600773", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:39:19.552090Z", - "iopub.status.busy": "2023-08-07T00:39:19.552004Z", - "iopub.status.idle": "2023-08-07T00:39:19.554743Z", - "shell.execute_reply": "2023-08-07T00:39:19.554473Z" + "iopub.execute_input": "2023-08-21T02:34:06.126460Z", + "iopub.status.busy": "2023-08-21T02:34:06.126346Z", + "iopub.status.idle": "2023-08-21T02:34:06.129561Z", + "shell.execute_reply": "2023-08-21T02:34:06.129124Z" } }, "outputs": [ @@ -1524,7 +1526,7 @@ }, { "cell_type": "markdown", - "id": "8767f70c", + "id": "32e600ff", "metadata": {}, "source": [ "The next line generates Figure 13.11, which is similar\n", @@ -1535,13 +1537,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "28c276b6", + "id": "924b7705", "metadata": { "execution": { - "iopub.execute_input": "2023-08-07T00:39:19.556715Z", - "iopub.status.busy": "2023-08-07T00:39:19.556515Z", - "iopub.status.idle": "2023-08-07T00:39:19.650514Z", - "shell.execute_reply": "2023-08-07T00:39:19.650181Z" + "iopub.execute_input": "2023-08-21T02:34:06.131323Z", + "iopub.status.busy": "2023-08-21T02:34:06.131207Z", + "iopub.status.idle": "2023-08-21T02:34:06.216626Z", + "shell.execute_reply": "2023-08-21T02:34:06.216270Z" }, "lines_to_next_cell": 0 }, @@ -1566,10 +1568,10 @@ }, { "cell_type": "markdown", - "id": "e4b5d621", + "id": "b9f54695", "metadata": {}, "source": [ - "\n" + " \n" ] } ], diff --git a/README.md b/README.md index c8867b2..e3d1f97 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,23 @@ # ISLP_labs -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.1) Up-to-date version of labs for ISLP. This repo will track labs for ISLP as their source code changes. The intent is that building a conda environment with -`requirements.txt` will reproduce -the results in this repo. +`requirements.txt` will reproduce the results in this repo. To install the current version of the requirements run ``` -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2/requirements.txt; +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2.1/requirements.txt; ``` The labs can now be run from this directory: ``` -cd notebook; jupyter lab Ch02-statlearning-lab.ipynb ``` diff --git a/requirements.txt b/requirements.txt index f591d4d..c917f5a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,17 +1,16 @@ -numpy>=1.7.1 -scipy>=0.9 -pandas>=0.20 -pandas<=1.9 -lxml # pandas needs this for html -scikit-learn>=1.2 -joblib -statsmodels>=0.13 -lifelines -pygam # for GAM in Ch7 -l0bnb==1.0.0 # for bestsubsets -ISLP -torch -pytorch_lightning -torchmetrics -torchvision -torchinfo +numpy==1.24.2 +scipy==1.11.1 +pandas==1.5.3 +lxml==4.9.3 +scikit-learn==1.3.0 +joblib==1.3.1 +statsmodels==0.14.0 +lifelines==0.27.7 +pygam==0.9.0 +l0bnb==1.0.0 +torch==2.0.1 +torchvision==0.15.2 +pytorch-lightning==2.0.6 +torchinfo==1.8.0 +torchmetrics==1.0.1 +ISLP==0.3.19 From 353df68ce0ccf2919e65bec482a58d93079a50c0 Mon Sep 17 00:00:00 2001 From: Thomas Schmelzer Date: Mon, 21 Aug 2023 13:00:43 -0700 Subject: [PATCH 02/18] Construction and Deployment of a Docker image (#5) Github action to create a docker file * Using manual dispatch, updated user name for docker * Update .dockerignore --------- Co-authored-by: Jonathan Taylor --- .dockerignore | 5 +++++ .github/workflows/docker.yml | 35 +++++++++++++++++++++++++++++++++++ docker/Dockerfile | 11 +++++++++++ 3 files changed, 51 insertions(+) create mode 100644 .dockerignore create mode 100644 .github/workflows/docker.yml create mode 100644 docker/Dockerfile diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..0814f57 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,5 @@ +.github +.git +docker +.dockerignore +*.Rmd diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml new file mode 100644 index 0000000..d03a410 --- /dev/null +++ b/.github/workflows/docker.yml @@ -0,0 +1,35 @@ +name: Publish Docker image + +on: + workflow_dispatch: + inputs: null + +jobs: + + push_to_registry: + name: Push Docker image to Docker Hub + runs-on: ubuntu-latest + steps: + - name: Check out the repo + uses: actions/checkout@v3 + + - name: Log in to Docker Hub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKER_USERNAME }} + password: ${{ secrets.DOCKER_PASSWORD }} + + - name: Extract metadata (tags, labels) for Docker + id: meta + uses: docker/metadata-action@v4 + with: + images: jetaylor74/ISLP_labs + + - name: Build and push Docker image + uses: docker/build-push-action@v4 + with: + context: . + file: docker/Dockerfile + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..d6c7075 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,11 @@ +FROM docker.io/jupyter/base-notebook:lab-4.0.5 + +COPY requirements.txt . + +# Install Python deps, the user approach is documented here: +# https://pythonspeed.com/articles/multi-stage-docker-python/ +RUN pip install --user --no-cache-dir -r requirements.txt + +COPY --chown=jovyan:users . ${HOME}/work + + From f9455fc5a135448af4f9dde341ec379aca721b8c Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:08:07 -0700 Subject: [PATCH 03/18] using ref to checkout --- .github/workflows/docker.yml | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index d03a410..2a7b6b0 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -2,7 +2,12 @@ name: Publish Docker image on: workflow_dispatch: - inputs: null + inputs: + LABS: + description: 'Labs version' + required: true + default: 'v2.1' + type: string jobs: @@ -12,7 +17,9 @@ jobs: steps: - name: Check out the repo uses: actions/checkout@v3 - + with: + ref: ${{ inputs.LABS }} + - name: Log in to Docker Hub uses: docker/login-action@v2 with: From f48e9ab55e0c4da9d93c6a5a680475325901f172 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:09:46 -0700 Subject: [PATCH 04/18] syntax error --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 2a7b6b0..1fbab9d 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -19,7 +19,7 @@ jobs: uses: actions/checkout@v3 with: ref: ${{ inputs.LABS }} - + - name: Log in to Docker Hub uses: docker/login-action@v2 with: From 73cf69f925398de41b147eee1d2a955fce936602 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:11:53 -0700 Subject: [PATCH 05/18] tag must be after a commit with docker action --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 1fbab9d..6ead2f8 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -6,7 +6,7 @@ on: LABS: description: 'Labs version' required: true - default: 'v2.1' + default: 'v2.1.1' type: string jobs: From 0bcd786c2abee8c3f28aecd433d2ff474bcf1518 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:23:30 -0700 Subject: [PATCH 06/18] trying metadata tags --- .github/workflows/docker.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 6ead2f8..1f91668 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -31,6 +31,8 @@ jobs: uses: docker/metadata-action@v4 with: images: jetaylor74/ISLP_labs + tags: + type=semver,pattern={{raw}} - name: Build and push Docker image uses: docker/build-push-action@v4 From 3c748c15fe19ba755e07efd7292b92cf56f9f1ae Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:27:32 -0700 Subject: [PATCH 07/18] order matters? --- .github/workflows/docker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 1f91668..8f092c5 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -30,9 +30,9 @@ jobs: id: meta uses: docker/metadata-action@v4 with: - images: jetaylor74/ISLP_labs - tags: + tags: | type=semver,pattern={{raw}} + images: jetaylor74/islp_labs - name: Build and push Docker image uses: docker/build-push-action@v4 From 91a9d76efb95fa0b9bf2d9e29c85e56fc25fe77e Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:38:55 -0700 Subject: [PATCH 08/18] using push event --- .github/workflows/docker.yml | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 8f092c5..e880b45 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -2,13 +2,12 @@ name: Publish Docker image on: workflow_dispatch: - inputs: - LABS: - description: 'Labs version' - required: true - default: 'v2.1.1' - type: string - + push: + tags: + - 'v*' + branches: + - 'stable' + jobs: push_to_registry: @@ -17,8 +16,6 @@ jobs: steps: - name: Check out the repo uses: actions/checkout@v3 - with: - ref: ${{ inputs.LABS }} - name: Log in to Docker Hub uses: docker/login-action@v2 @@ -30,8 +27,6 @@ jobs: id: meta uses: docker/metadata-action@v4 with: - tags: | - type=semver,pattern={{raw}} images: jetaylor74/islp_labs - name: Build and push Docker image From e3e48ab5c69098c320cfef26040b82b09c68f779 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:45:24 -0700 Subject: [PATCH 09/18] updating tags based on https://github.com/marketplace/actions/docker-metadata-action --- .github/workflows/docker.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index e880b45..3214034 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -28,7 +28,10 @@ jobs: uses: docker/metadata-action@v4 with: images: jetaylor74/islp_labs - + tags: | + type=ref,event=branch + type=semver,pattern={{raw}} + - name: Build and push Docker image uses: docker/build-push-action@v4 with: From 106236b5dbb86c6ca6c341bbd16e5d3580c11fcc Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:46:14 -0700 Subject: [PATCH 10/18] whitespace --- .github/workflows/docker.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 3214034..6180344 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -31,7 +31,7 @@ jobs: tags: | type=ref,event=branch type=semver,pattern={{raw}} - + - name: Build and push Docker image uses: docker/build-push-action@v4 with: From 9d84224629ab9f5ac0625eaf1a49adb21d9896d7 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 13:57:15 -0700 Subject: [PATCH 11/18] removing quotes based on https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpushbranchestagsbranches-ignoretags-ignore --- .github/workflows/docker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 6180344..fc90583 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -4,9 +4,9 @@ on: workflow_dispatch: push: tags: - - 'v*' + - v* branches: - - 'stable' + - stable jobs: From 58b6936b0e1fa8f8b7290bef5b65b03f3842ee94 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 14:10:27 -0700 Subject: [PATCH 12/18] only the stable branch push --- .github/workflows/docker.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index fc90583..436517a 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -3,8 +3,6 @@ name: Publish Docker image on: workflow_dispatch: push: - tags: - - v* branches: - stable From 7c19e32f0a466a27c91c585796fede09ea5868a0 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Mon, 21 Aug 2023 17:41:26 -0700 Subject: [PATCH 13/18] docker to .docker --- {docker => .docker}/Dockerfile | 0 .dockerignore | 2 +- .github/workflows/docker.yml | 2 +- 3 files changed, 2 insertions(+), 2 deletions(-) rename {docker => .docker}/Dockerfile (100%) diff --git a/docker/Dockerfile b/.docker/Dockerfile similarity index 100% rename from docker/Dockerfile rename to .docker/Dockerfile diff --git a/.dockerignore b/.dockerignore index 0814f57..a4b83e2 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,5 +1,5 @@ .github .git -docker +.docker .dockerignore *.Rmd diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 436517a..cabfe07 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -34,7 +34,7 @@ jobs: uses: docker/build-push-action@v4 with: context: . - file: docker/Dockerfile + file: .docker/Dockerfile push: true tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} From 1583d66bfa4b9a14fd77549658b53a09d7171fbb Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:06:02 -0700 Subject: [PATCH 14/18] trevor's edits 0822 --- .github/workflows/docker.yml | 4 +- Ch02-statlearn-lab.Rmd | 2 +- Ch02-statlearn-lab.ipynb | 946 +- Ch03-linreg-lab.Rmd | 2 +- Ch03-linreg-lab.ipynb | 307 +- Ch04-classification-lab.Rmd | 2 +- Ch04-classification-lab.ipynb | 638 +- Ch05-resample-lab.Rmd | 2 +- Ch05-resample-lab.ipynb | 212 +- Ch06-varselect-lab.Rmd | 2 +- Ch06-varselect-lab.ipynb | 7086 +- Ch07-nonlin-lab.Rmd | 2 +- Ch07-nonlin-lab.ipynb | 398 +- Ch08-baggboost-lab.Rmd | 2 +- Ch08-baggboost-lab.ipynb | 286 +- Ch09-svm-lab.Rmd | 2 +- Ch09-svm-lab.ipynb | 278 +- Ch10-deeplearning-lab.Rmd | 16 + Ch10-deeplearning-lab.ipynb | 170204 ++++++++++++++++++++++++++++++- Ch11-surv-lab.Rmd | 2 +- Ch11-surv-lab.ipynb | 284 +- Ch12-unsup-lab.Rmd | 2 +- Ch12-unsup-lab.ipynb | 468 +- Ch13-multiple-lab.Rmd | 2 +- Ch13-multiple-lab.ipynb | 252 +- 25 files changed, 175088 insertions(+), 6313 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index cabfe07..207016d 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -5,7 +5,9 @@ on: push: branches: - stable - + tags: + - v* + jobs: push_to_registry: diff --git a/Ch02-statlearn-lab.Rmd b/Ch02-statlearn-lab.Rmd index 556bbbf..67c88ef 100644 --- a/Ch02-statlearn-lab.Rmd +++ b/Ch02-statlearn-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb index f137f1c..9f202fe 100644 --- a/Ch02-statlearn-lab.ipynb +++ b/Ch02-statlearn-lab.ipynb @@ -102,10 +102,10 @@ "id": "9e8aa21f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.654555Z", - "iopub.status.busy": "2023-08-21T03:36:04.654242Z", - "iopub.status.idle": "2023-08-21T03:36:04.664431Z", - "shell.execute_reply": "2023-08-21T03:36:04.663990Z" + "iopub.execute_input": "2023-08-22T06:59:05.520475Z", + "iopub.status.busy": "2023-08-22T06:59:05.520373Z", + "iopub.status.idle": "2023-08-22T06:59:05.524957Z", + "shell.execute_reply": "2023-08-22T06:59:05.524661Z" } }, "outputs": [ @@ -135,10 +135,10 @@ "id": "d62ec119", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.666908Z", - "iopub.status.busy": "2023-08-21T03:36:04.666727Z", - "iopub.status.idle": "2023-08-21T03:36:04.693658Z", - "shell.execute_reply": "2023-08-21T03:36:04.693389Z" + "iopub.execute_input": "2023-08-22T06:59:05.526569Z", + "iopub.status.busy": "2023-08-22T06:59:05.526459Z", + "iopub.status.idle": "2023-08-22T06:59:05.528480Z", + "shell.execute_reply": "2023-08-22T06:59:05.528197Z" } }, "outputs": [], @@ -160,10 +160,10 @@ "id": "c64e9f4d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.695168Z", - "iopub.status.busy": "2023-08-21T03:36:04.695071Z", - "iopub.status.idle": "2023-08-21T03:36:04.697996Z", - "shell.execute_reply": "2023-08-21T03:36:04.697736Z" + "iopub.execute_input": "2023-08-22T06:59:05.530498Z", + "iopub.status.busy": "2023-08-22T06:59:05.530245Z", + "iopub.status.idle": "2023-08-22T06:59:05.533462Z", + "shell.execute_reply": "2023-08-22T06:59:05.533113Z" } }, "outputs": [ @@ -200,10 +200,10 @@ "id": "9abccc1f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.699502Z", - "iopub.status.busy": "2023-08-21T03:36:04.699420Z", - "iopub.status.idle": "2023-08-21T03:36:04.701419Z", - "shell.execute_reply": "2023-08-21T03:36:04.701183Z" + "iopub.execute_input": "2023-08-22T06:59:05.535049Z", + "iopub.status.busy": "2023-08-22T06:59:05.534962Z", + "iopub.status.idle": "2023-08-22T06:59:05.537010Z", + "shell.execute_reply": "2023-08-22T06:59:05.536734Z" } }, "outputs": [ @@ -249,10 +249,10 @@ "id": "802ca33c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.702877Z", - "iopub.status.busy": "2023-08-21T03:36:04.702786Z", - "iopub.status.idle": "2023-08-21T03:36:04.704849Z", - "shell.execute_reply": "2023-08-21T03:36:04.704596Z" + "iopub.execute_input": "2023-08-22T06:59:05.538667Z", + "iopub.status.busy": "2023-08-22T06:59:05.538580Z", + "iopub.status.idle": "2023-08-22T06:59:05.541098Z", + "shell.execute_reply": "2023-08-22T06:59:05.540721Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "a8c72744", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.706312Z", - "iopub.status.busy": "2023-08-21T03:36:04.706207Z", - "iopub.status.idle": "2023-08-21T03:36:04.708359Z", - "shell.execute_reply": "2023-08-21T03:36:04.708116Z" + "iopub.execute_input": "2023-08-22T06:59:05.542902Z", + "iopub.status.busy": "2023-08-22T06:59:05.542804Z", + "iopub.status.idle": "2023-08-22T06:59:05.545269Z", + "shell.execute_reply": "2023-08-22T06:59:05.544991Z" } }, "outputs": [ @@ -364,10 +364,10 @@ "id": "f1c7d1db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.709873Z", - "iopub.status.busy": "2023-08-21T03:36:04.709781Z", - "iopub.status.idle": "2023-08-21T03:36:04.767425Z", - "shell.execute_reply": "2023-08-21T03:36:04.766663Z" + "iopub.execute_input": "2023-08-22T06:59:05.546995Z", + "iopub.status.busy": "2023-08-22T06:59:05.546897Z", + "iopub.status.idle": "2023-08-22T06:59:07.928068Z", + "shell.execute_reply": "2023-08-22T06:59:07.927627Z" }, "lines_to_next_cell": 0 }, @@ -400,10 +400,10 @@ "id": "e2ea2bfd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.772015Z", - "iopub.status.busy": "2023-08-21T03:36:04.771631Z", - "iopub.status.idle": "2023-08-21T03:36:04.777911Z", - "shell.execute_reply": "2023-08-21T03:36:04.777302Z" + "iopub.execute_input": "2023-08-22T06:59:07.930503Z", + "iopub.status.busy": "2023-08-22T06:59:07.930338Z", + "iopub.status.idle": "2023-08-22T06:59:07.932349Z", + "shell.execute_reply": "2023-08-22T06:59:07.932076Z" }, "lines_to_next_cell": 0 }, @@ -439,10 +439,10 @@ "id": "59fbf9fd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.781791Z", - "iopub.status.busy": "2023-08-21T03:36:04.781430Z", - "iopub.status.idle": "2023-08-21T03:36:04.788256Z", - "shell.execute_reply": "2023-08-21T03:36:04.787586Z" + "iopub.execute_input": "2023-08-22T06:59:07.934561Z", + "iopub.status.busy": "2023-08-22T06:59:07.934430Z", + "iopub.status.idle": "2023-08-22T06:59:07.936902Z", + "shell.execute_reply": "2023-08-22T06:59:07.936605Z" }, "lines_to_next_cell": 0 }, @@ -486,10 +486,10 @@ "id": "2279437e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.792995Z", - "iopub.status.busy": "2023-08-21T03:36:04.792549Z", - "iopub.status.idle": "2023-08-21T03:36:04.800387Z", - "shell.execute_reply": "2023-08-21T03:36:04.799738Z" + "iopub.execute_input": "2023-08-22T06:59:07.939184Z", + "iopub.status.busy": "2023-08-22T06:59:07.938968Z", + "iopub.status.idle": "2023-08-22T06:59:07.941378Z", + "shell.execute_reply": "2023-08-22T06:59:07.941135Z" }, "lines_to_next_cell": 0 }, @@ -537,10 +537,10 @@ "id": "75bf1b1e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.804503Z", - "iopub.status.busy": "2023-08-21T03:36:04.804231Z", - "iopub.status.idle": "2023-08-21T03:36:04.810136Z", - "shell.execute_reply": "2023-08-21T03:36:04.809353Z" + "iopub.execute_input": "2023-08-22T06:59:07.943172Z", + "iopub.status.busy": "2023-08-22T06:59:07.942965Z", + "iopub.status.idle": "2023-08-22T06:59:07.945452Z", + "shell.execute_reply": "2023-08-22T06:59:07.945026Z" } }, "outputs": [ @@ -575,10 +575,10 @@ "id": "58292240", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.813981Z", - "iopub.status.busy": "2023-08-21T03:36:04.813417Z", - "iopub.status.idle": "2023-08-21T03:36:04.819511Z", - "shell.execute_reply": "2023-08-21T03:36:04.818936Z" + "iopub.execute_input": "2023-08-22T06:59:07.947238Z", + "iopub.status.busy": "2023-08-22T06:59:07.947112Z", + "iopub.status.idle": "2023-08-22T06:59:07.949367Z", + "shell.execute_reply": "2023-08-22T06:59:07.949033Z" }, "lines_to_next_cell": 0 }, @@ -615,10 +615,10 @@ "id": "fc5fff57", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.823519Z", - "iopub.status.busy": "2023-08-21T03:36:04.823222Z", - "iopub.status.idle": "2023-08-21T03:36:04.829861Z", - "shell.execute_reply": "2023-08-21T03:36:04.829223Z" + "iopub.execute_input": "2023-08-22T06:59:07.951095Z", + "iopub.status.busy": "2023-08-22T06:59:07.950962Z", + "iopub.status.idle": "2023-08-22T06:59:07.953323Z", + "shell.execute_reply": "2023-08-22T06:59:07.953058Z" }, "lines_to_next_cell": 2 }, @@ -654,10 +654,10 @@ "id": "762562a6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.833747Z", - "iopub.status.busy": "2023-08-21T03:36:04.833468Z", - "iopub.status.idle": "2023-08-21T03:36:04.838421Z", - "shell.execute_reply": "2023-08-21T03:36:04.837747Z" + "iopub.execute_input": "2023-08-22T06:59:07.955159Z", + "iopub.status.busy": "2023-08-22T06:59:07.955029Z", + "iopub.status.idle": "2023-08-22T06:59:07.957291Z", + "shell.execute_reply": "2023-08-22T06:59:07.956944Z" }, "lines_to_next_cell": 0 }, @@ -680,10 +680,10 @@ "id": "66d2b82a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.842127Z", - "iopub.status.busy": "2023-08-21T03:36:04.841891Z", - "iopub.status.idle": "2023-08-21T03:36:04.848885Z", - "shell.execute_reply": "2023-08-21T03:36:04.848125Z" + "iopub.execute_input": "2023-08-22T06:59:07.959022Z", + "iopub.status.busy": "2023-08-22T06:59:07.958921Z", + "iopub.status.idle": "2023-08-22T06:59:07.961144Z", + "shell.execute_reply": "2023-08-22T06:59:07.960798Z" }, "lines_to_next_cell": 2 }, @@ -718,10 +718,10 @@ "id": "89881402", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.853039Z", - "iopub.status.busy": "2023-08-21T03:36:04.852643Z", - "iopub.status.idle": "2023-08-21T03:36:04.859072Z", - "shell.execute_reply": "2023-08-21T03:36:04.858149Z" + "iopub.execute_input": "2023-08-22T06:59:07.963075Z", + "iopub.status.busy": "2023-08-22T06:59:07.962934Z", + "iopub.status.idle": "2023-08-22T06:59:07.965265Z", + "shell.execute_reply": "2023-08-22T06:59:07.964978Z" }, "lines_to_next_cell": 2 }, @@ -761,10 +761,10 @@ "id": "0572d3f6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.863011Z", - "iopub.status.busy": "2023-08-21T03:36:04.862610Z", - "iopub.status.idle": "2023-08-21T03:36:04.869722Z", - "shell.execute_reply": "2023-08-21T03:36:04.869078Z" + "iopub.execute_input": "2023-08-22T06:59:07.967468Z", + "iopub.status.busy": "2023-08-22T06:59:07.967139Z", + "iopub.status.idle": "2023-08-22T06:59:07.969644Z", + "shell.execute_reply": "2023-08-22T06:59:07.969386Z" }, "lines_to_next_cell": 0 }, @@ -799,10 +799,10 @@ "id": "33b10a6f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.874228Z", - "iopub.status.busy": "2023-08-21T03:36:04.873632Z", - "iopub.status.idle": "2023-08-21T03:36:04.879222Z", - "shell.execute_reply": "2023-08-21T03:36:04.878606Z" + "iopub.execute_input": "2023-08-22T06:59:07.971652Z", + "iopub.status.busy": "2023-08-22T06:59:07.971489Z", + "iopub.status.idle": "2023-08-22T06:59:07.974229Z", + "shell.execute_reply": "2023-08-22T06:59:07.973900Z" }, "lines_to_next_cell": 0 }, @@ -845,10 +845,10 @@ "id": "a32716db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.882833Z", - "iopub.status.busy": "2023-08-21T03:36:04.882354Z", - "iopub.status.idle": "2023-08-21T03:36:04.887384Z", - "shell.execute_reply": "2023-08-21T03:36:04.886127Z" + "iopub.execute_input": "2023-08-22T06:59:07.976214Z", + "iopub.status.busy": "2023-08-22T06:59:07.976066Z", + "iopub.status.idle": "2023-08-22T06:59:07.978551Z", + "shell.execute_reply": "2023-08-22T06:59:07.978148Z" } }, "outputs": [ @@ -896,10 +896,10 @@ "id": "3db6e1cf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.890861Z", - "iopub.status.busy": "2023-08-21T03:36:04.890532Z", - "iopub.status.idle": "2023-08-21T03:36:04.894815Z", - "shell.execute_reply": "2023-08-21T03:36:04.894149Z" + "iopub.execute_input": "2023-08-22T06:59:07.980462Z", + "iopub.status.busy": "2023-08-22T06:59:07.980329Z", + "iopub.status.idle": "2023-08-22T06:59:07.982878Z", + "shell.execute_reply": "2023-08-22T06:59:07.982438Z" }, "lines_to_next_cell": 0 }, @@ -934,10 +934,10 @@ "id": "e15c753f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.898049Z", - "iopub.status.busy": "2023-08-21T03:36:04.897796Z", - "iopub.status.idle": "2023-08-21T03:36:04.902137Z", - "shell.execute_reply": "2023-08-21T03:36:04.901538Z" + "iopub.execute_input": "2023-08-22T06:59:07.984961Z", + "iopub.status.busy": "2023-08-22T06:59:07.984816Z", + "iopub.status.idle": "2023-08-22T06:59:07.987101Z", + "shell.execute_reply": "2023-08-22T06:59:07.986805Z" }, "lines_to_next_cell": 0 }, @@ -975,10 +975,10 @@ "id": "91c6e7d8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.905358Z", - "iopub.status.busy": "2023-08-21T03:36:04.905148Z", - "iopub.status.idle": "2023-08-21T03:36:04.910143Z", - "shell.execute_reply": "2023-08-21T03:36:04.909500Z" + "iopub.execute_input": "2023-08-22T06:59:07.989269Z", + "iopub.status.busy": "2023-08-22T06:59:07.988940Z", + "iopub.status.idle": "2023-08-22T06:59:07.991567Z", + "shell.execute_reply": "2023-08-22T06:59:07.991233Z" } }, "outputs": [ @@ -1033,10 +1033,10 @@ "id": "59d95dce", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:04.913411Z", - "iopub.status.busy": "2023-08-21T03:36:04.912985Z", - "iopub.status.idle": "2023-08-21T03:36:05.037046Z", - "shell.execute_reply": "2023-08-21T03:36:05.036755Z" + "iopub.execute_input": "2023-08-22T06:59:07.993423Z", + "iopub.status.busy": "2023-08-22T06:59:07.993299Z", + "iopub.status.idle": "2023-08-22T06:59:08.133462Z", + "shell.execute_reply": "2023-08-22T06:59:08.132910Z" }, "lines_to_next_cell": 2 }, @@ -1073,10 +1073,10 @@ "id": "a6fde9af", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.038645Z", - "iopub.status.busy": "2023-08-21T03:36:05.038536Z", - "iopub.status.idle": "2023-08-21T03:36:05.040823Z", - "shell.execute_reply": "2023-08-21T03:36:05.040589Z" + "iopub.execute_input": "2023-08-22T06:59:08.135655Z", + "iopub.status.busy": "2023-08-22T06:59:08.135507Z", + "iopub.status.idle": "2023-08-22T06:59:08.138481Z", + "shell.execute_reply": "2023-08-22T06:59:08.138180Z" } }, "outputs": [ @@ -1117,10 +1117,10 @@ "id": "fadb6b45", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.042203Z", - "iopub.status.busy": "2023-08-21T03:36:05.042112Z", - "iopub.status.idle": "2023-08-21T03:36:05.044185Z", - "shell.execute_reply": "2023-08-21T03:36:05.043940Z" + "iopub.execute_input": "2023-08-22T06:59:08.140173Z", + "iopub.status.busy": "2023-08-22T06:59:08.140051Z", + "iopub.status.idle": "2023-08-22T06:59:08.142777Z", + "shell.execute_reply": "2023-08-22T06:59:08.142404Z" } }, "outputs": [ @@ -1154,10 +1154,10 @@ "id": "fda3134b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.045709Z", - "iopub.status.busy": "2023-08-21T03:36:05.045601Z", - "iopub.status.idle": "2023-08-21T03:36:05.047665Z", - "shell.execute_reply": "2023-08-21T03:36:05.047412Z" + "iopub.execute_input": "2023-08-22T06:59:08.144782Z", + "iopub.status.busy": "2023-08-22T06:59:08.144640Z", + "iopub.status.idle": "2023-08-22T06:59:08.147297Z", + "shell.execute_reply": "2023-08-22T06:59:08.146996Z" } }, "outputs": [ @@ -1190,10 +1190,10 @@ "id": "52eb335b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.049290Z", - "iopub.status.busy": "2023-08-21T03:36:05.049191Z", - "iopub.status.idle": "2023-08-21T03:36:05.051249Z", - "shell.execute_reply": "2023-08-21T03:36:05.051004Z" + "iopub.execute_input": "2023-08-22T06:59:08.148995Z", + "iopub.status.busy": "2023-08-22T06:59:08.148870Z", + "iopub.status.idle": "2023-08-22T06:59:08.151571Z", + "shell.execute_reply": "2023-08-22T06:59:08.151251Z" }, "lines_to_next_cell": 2 }, @@ -1237,26 +1237,26 @@ "id": "ac5e9d29", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.052622Z", - "iopub.status.busy": "2023-08-21T03:36:05.052529Z", - "iopub.status.idle": "2023-08-21T03:36:05.054818Z", - "shell.execute_reply": "2023-08-21T03:36:05.054569Z" + "iopub.execute_input": "2023-08-22T06:59:08.154113Z", + "iopub.status.busy": "2023-08-22T06:59:08.153958Z", + "iopub.status.idle": "2023-08-22T06:59:08.157534Z", + "shell.execute_reply": "2023-08-22T06:59:08.156973Z" } }, "outputs": [ { "data": { "text/plain": [ - "array([-0.97992307, 0.29561095, 0.60481929, -0.20482365, -1.01517527,\n", - " 2.76594674, -0.65748447, 1.27396181, -1.01573869, 1.37373688,\n", - " 0.93524901, -2.41971622, -0.58080017, 0.13777341, 1.35936356,\n", - " 0.61747629, 0.82550921, 0.7943598 , 1.0905082 , 1.80820045,\n", - " -1.31320321, 2.0651966 , -0.78632522, -2.01068042, -1.36562571,\n", - " 1.43466837, -0.16848093, -1.57211786, 0.49888498, -1.86050966,\n", - " 1.08743609, 0.53059599, 0.02172848, -0.41808062, 1.88343414,\n", - " 0.09112977, 1.23489568, 0.53207714, -1.62670383, 1.29859234,\n", - " 0.30311344, 1.01143392, -1.64596169, 1.58876421, -1.85189683,\n", - " -0.53929878, -2.19895144, -0.36691225, 1.03621761, 0.25278481])" + "array([-0.83511556, 0.43461482, -1.3810022 , -0.64162363, -0.86270682,\n", + " 0.6660104 , 0.17543353, 0.68220139, -0.20392851, -1.47534629,\n", + " 0.18438201, 1.50157883, 0.47473968, -1.33062844, 1.57614184,\n", + " -0.25596784, 0.66848396, -1.5410464 , 0.25298297, -0.79118181,\n", + " -0.49399731, -1.45139182, 0.71261617, 1.19862796, 0.52772362,\n", + " 0.57343239, -0.01577204, 2.56032683, -0.18936979, -0.09651695,\n", + " -1.74331796, 0.11390389, 0.27595492, 1.2606086 , 0.49282705,\n", + " 0.60603827, 0.24699795, 0.28636264, 0.54683924, 0.45133321,\n", + " 0.92563958, 1.20847512, 0.40993092, 0.01951345, 0.27271415,\n", + " 0.94231124, 0.47471813, 2.12851232, 0.128139 , -0.56681371])" ] }, "execution_count": 28, @@ -1283,10 +1283,10 @@ "id": "55fa905e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.056219Z", - "iopub.status.busy": "2023-08-21T03:36:05.056124Z", - "iopub.status.idle": "2023-08-21T03:36:05.057790Z", - "shell.execute_reply": "2023-08-21T03:36:05.057543Z" + "iopub.execute_input": "2023-08-22T06:59:08.159589Z", + "iopub.status.busy": "2023-08-22T06:59:08.159437Z", + "iopub.status.idle": "2023-08-22T06:59:08.161722Z", + "shell.execute_reply": "2023-08-22T06:59:08.161369Z" }, "lines_to_next_cell": 0 }, @@ -1310,18 +1310,18 @@ "id": "fde0dc19", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.059163Z", - "iopub.status.busy": "2023-08-21T03:36:05.059091Z", - "iopub.status.idle": "2023-08-21T03:36:05.061351Z", - "shell.execute_reply": "2023-08-21T03:36:05.061105Z" + "iopub.execute_input": "2023-08-22T06:59:08.164153Z", + "iopub.status.busy": "2023-08-22T06:59:08.163979Z", + "iopub.status.idle": "2023-08-22T06:59:08.168094Z", + "shell.execute_reply": "2023-08-22T06:59:08.167676Z" } }, "outputs": [ { "data": { "text/plain": [ - "array([[1. , 0.66045794],\n", - " [0.66045794, 1. ]])" + "array([[1. , 0.65985439],\n", + " [0.65985439, 1. ]])" ] }, "execution_count": 30, @@ -1350,10 +1350,10 @@ "id": "5099cf54", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.062721Z", - "iopub.status.busy": "2023-08-21T03:36:05.062630Z", - "iopub.status.idle": "2023-08-21T03:36:05.064539Z", - "shell.execute_reply": "2023-08-21T03:36:05.064300Z" + "iopub.execute_input": "2023-08-22T06:59:08.169980Z", + "iopub.status.busy": "2023-08-22T06:59:08.169865Z", + "iopub.status.idle": "2023-08-22T06:59:08.173468Z", + "shell.execute_reply": "2023-08-22T06:59:08.172857Z" }, "lines_to_next_cell": 0 }, @@ -1362,8 +1362,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[-9.63514647 -0.12742473]\n", - "[0.85490033 0.05488893]\n" + "[ 6.33922704 -0.66068102]\n", + "[-6.96104352 18.61644142]\n" ] } ], @@ -1401,10 +1401,10 @@ "id": "9d8074e5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.065999Z", - "iopub.status.busy": "2023-08-21T03:36:05.065912Z", - "iopub.status.idle": "2023-08-21T03:36:05.068039Z", - "shell.execute_reply": "2023-08-21T03:36:05.067820Z" + "iopub.execute_input": "2023-08-22T06:59:08.175381Z", + "iopub.status.busy": "2023-08-22T06:59:08.175239Z", + "iopub.status.idle": "2023-08-22T06:59:08.177926Z", + "shell.execute_reply": "2023-08-22T06:59:08.177603Z" } }, "outputs": [ @@ -1447,10 +1447,10 @@ "id": "e98472df", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.069438Z", - "iopub.status.busy": "2023-08-21T03:36:05.069347Z", - "iopub.status.idle": "2023-08-21T03:36:05.071564Z", - "shell.execute_reply": "2023-08-21T03:36:05.071324Z" + "iopub.execute_input": "2023-08-22T06:59:08.179784Z", + "iopub.status.busy": "2023-08-22T06:59:08.179647Z", + "iopub.status.idle": "2023-08-22T06:59:08.182540Z", + "shell.execute_reply": "2023-08-22T06:59:08.182002Z" }, "lines_to_next_cell": 0 }, @@ -1486,10 +1486,10 @@ "id": "8c2784fd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.072967Z", - "iopub.status.busy": "2023-08-21T03:36:05.072873Z", - "iopub.status.idle": "2023-08-21T03:36:05.075053Z", - "shell.execute_reply": "2023-08-21T03:36:05.074833Z" + "iopub.execute_input": "2023-08-22T06:59:08.184240Z", + "iopub.status.busy": "2023-08-22T06:59:08.184117Z", + "iopub.status.idle": "2023-08-22T06:59:08.186691Z", + "shell.execute_reply": "2023-08-22T06:59:08.186367Z" }, "lines_to_next_cell": 2 }, @@ -1524,10 +1524,10 @@ "id": "7e7205f2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.076473Z", - "iopub.status.busy": "2023-08-21T03:36:05.076373Z", - "iopub.status.idle": "2023-08-21T03:36:05.078474Z", - "shell.execute_reply": "2023-08-21T03:36:05.078224Z" + "iopub.execute_input": "2023-08-22T06:59:08.188421Z", + "iopub.status.busy": "2023-08-22T06:59:08.188291Z", + "iopub.status.idle": "2023-08-22T06:59:08.191242Z", + "shell.execute_reply": "2023-08-22T06:59:08.190935Z" } }, "outputs": [ @@ -1561,10 +1561,10 @@ "id": "fce06849", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.079856Z", - "iopub.status.busy": "2023-08-21T03:36:05.079755Z", - "iopub.status.idle": "2023-08-21T03:36:05.081961Z", - "shell.execute_reply": "2023-08-21T03:36:05.081709Z" + "iopub.execute_input": "2023-08-22T06:59:08.192889Z", + "iopub.status.busy": "2023-08-22T06:59:08.192762Z", + "iopub.status.idle": "2023-08-22T06:59:08.195517Z", + "shell.execute_reply": "2023-08-22T06:59:08.195194Z" } }, "outputs": [ @@ -1607,10 +1607,10 @@ "id": "1403ff7a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.083361Z", - "iopub.status.busy": "2023-08-21T03:36:05.083273Z", - "iopub.status.idle": "2023-08-21T03:36:05.085366Z", - "shell.execute_reply": "2023-08-21T03:36:05.085112Z" + "iopub.execute_input": "2023-08-22T06:59:08.197212Z", + "iopub.status.busy": "2023-08-22T06:59:08.197063Z", + "iopub.status.idle": "2023-08-22T06:59:08.199463Z", + "shell.execute_reply": "2023-08-22T06:59:08.199082Z" } }, "outputs": [ @@ -1643,10 +1643,10 @@ "id": "7e9255ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.086730Z", - "iopub.status.busy": "2023-08-21T03:36:05.086636Z", - "iopub.status.idle": "2023-08-21T03:36:05.088698Z", - "shell.execute_reply": "2023-08-21T03:36:05.088470Z" + "iopub.execute_input": "2023-08-22T06:59:08.201135Z", + "iopub.status.busy": "2023-08-22T06:59:08.201013Z", + "iopub.status.idle": "2023-08-22T06:59:08.203278Z", + "shell.execute_reply": "2023-08-22T06:59:08.202984Z" }, "lines_to_next_cell": 0 }, @@ -1713,10 +1713,10 @@ "id": "8236e5f7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.090105Z", - "iopub.status.busy": "2023-08-21T03:36:05.090015Z", - "iopub.status.idle": "2023-08-21T03:36:05.390499Z", - "shell.execute_reply": "2023-08-21T03:36:05.390143Z" + "iopub.execute_input": "2023-08-22T06:59:08.205399Z", + "iopub.status.busy": "2023-08-22T06:59:08.205262Z", + "iopub.status.idle": "2023-08-22T06:59:09.267032Z", + "shell.execute_reply": "2023-08-22T06:59:09.266627Z" } }, "outputs": [ @@ -1755,10 +1755,10 @@ "id": "ddc9ed4f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.392242Z", - "iopub.status.busy": "2023-08-21T03:36:05.392127Z", - "iopub.status.idle": "2023-08-21T03:36:05.464507Z", - "shell.execute_reply": "2023-08-21T03:36:05.464208Z" + "iopub.execute_input": "2023-08-22T06:59:09.269192Z", + "iopub.status.busy": "2023-08-22T06:59:09.269015Z", + "iopub.status.idle": "2023-08-22T06:59:09.336385Z", + "shell.execute_reply": "2023-08-22T06:59:09.335994Z" } }, "outputs": [ @@ -1793,10 +1793,10 @@ "id": "c64ed600", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.466274Z", - "iopub.status.busy": "2023-08-21T03:36:05.466157Z", - "iopub.status.idle": "2023-08-21T03:36:05.540801Z", - "shell.execute_reply": "2023-08-21T03:36:05.540543Z" + "iopub.execute_input": "2023-08-22T06:59:09.338601Z", + "iopub.status.busy": "2023-08-22T06:59:09.338466Z", + "iopub.status.idle": "2023-08-22T06:59:09.408187Z", + "shell.execute_reply": "2023-08-22T06:59:09.407850Z" }, "lines_to_next_cell": 0 }, @@ -1841,10 +1841,10 @@ "id": "bc6245e2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.542497Z", - "iopub.status.busy": "2023-08-21T03:36:05.542378Z", - "iopub.status.idle": "2023-08-21T03:36:05.621061Z", - "shell.execute_reply": "2023-08-21T03:36:05.620765Z" + "iopub.execute_input": "2023-08-22T06:59:09.410036Z", + "iopub.status.busy": "2023-08-22T06:59:09.409914Z", + "iopub.status.idle": "2023-08-22T06:59:09.481803Z", + "shell.execute_reply": "2023-08-22T06:59:09.481516Z" } }, "outputs": [ @@ -1881,10 +1881,10 @@ "id": "2454807b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.622738Z", - "iopub.status.busy": "2023-08-21T03:36:05.622624Z", - "iopub.status.idle": "2023-08-21T03:36:05.700853Z", - "shell.execute_reply": "2023-08-21T03:36:05.700564Z" + "iopub.execute_input": "2023-08-22T06:59:09.483769Z", + "iopub.status.busy": "2023-08-22T06:59:09.483410Z", + "iopub.status.idle": "2023-08-22T06:59:09.583871Z", + "shell.execute_reply": "2023-08-22T06:59:09.583567Z" }, "lines_to_next_cell": 0 }, @@ -1892,7 +1892,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -1944,10 +1944,10 @@ "id": "1e18a793", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.702667Z", - "iopub.status.busy": "2023-08-21T03:36:05.702545Z", - "iopub.status.idle": "2023-08-21T03:36:05.797850Z", - "shell.execute_reply": "2023-08-21T03:36:05.797545Z" + "iopub.execute_input": "2023-08-22T06:59:09.585676Z", + "iopub.status.busy": "2023-08-22T06:59:09.585555Z", + "iopub.status.idle": "2023-08-22T06:59:09.674249Z", + "shell.execute_reply": "2023-08-22T06:59:09.673902Z" } }, "outputs": [ @@ -1985,10 +1985,10 @@ "id": "aec3f009", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.799658Z", - "iopub.status.busy": "2023-08-21T03:36:05.799529Z", - "iopub.status.idle": "2023-08-21T03:36:05.861021Z", - "shell.execute_reply": "2023-08-21T03:36:05.860749Z" + "iopub.execute_input": "2023-08-22T06:59:09.676005Z", + "iopub.status.busy": "2023-08-22T06:59:09.675879Z", + "iopub.status.idle": "2023-08-22T06:59:09.733152Z", + "shell.execute_reply": "2023-08-22T06:59:09.732799Z" }, "lines_to_next_cell": 0 }, @@ -2039,10 +2039,10 @@ "id": "2cbc7fd4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:05.862740Z", - "iopub.status.busy": "2023-08-21T03:36:05.862621Z", - "iopub.status.idle": "2023-08-21T03:36:06.143924Z", - "shell.execute_reply": "2023-08-21T03:36:06.143646Z" + "iopub.execute_input": "2023-08-22T06:59:09.734976Z", + "iopub.status.busy": "2023-08-22T06:59:09.734855Z", + "iopub.status.idle": "2023-08-22T06:59:09.987982Z", + "shell.execute_reply": "2023-08-22T06:59:09.987518Z" }, "lines_to_next_cell": 0 }, @@ -2079,10 +2079,10 @@ "id": "702f80d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:06.145683Z", - "iopub.status.busy": "2023-08-21T03:36:06.145574Z", - "iopub.status.idle": "2023-08-21T03:36:06.326301Z", - "shell.execute_reply": "2023-08-21T03:36:06.326007Z" + "iopub.execute_input": "2023-08-22T06:59:09.989943Z", + "iopub.status.busy": "2023-08-22T06:59:09.989799Z", + "iopub.status.idle": "2023-08-22T06:59:10.164367Z", + "shell.execute_reply": "2023-08-22T06:59:10.164050Z" }, "lines_to_next_cell": 0 }, @@ -2132,10 +2132,10 @@ "id": "5493d229", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:06.327958Z", - "iopub.status.busy": "2023-08-21T03:36:06.327847Z", - "iopub.status.idle": "2023-08-21T03:36:07.432805Z", - "shell.execute_reply": "2023-08-21T03:36:07.432429Z" + "iopub.execute_input": "2023-08-22T06:59:10.166485Z", + "iopub.status.busy": "2023-08-22T06:59:10.166339Z", + "iopub.status.idle": "2023-08-22T06:59:11.473366Z", + "shell.execute_reply": "2023-08-22T06:59:11.473053Z" }, "lines_to_next_cell": 2 }, @@ -2159,10 +2159,10 @@ "id": "bd07af12", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:07.434785Z", - "iopub.status.busy": "2023-08-21T03:36:07.434614Z", - "iopub.status.idle": "2023-08-21T03:36:07.663475Z", - "shell.execute_reply": "2023-08-21T03:36:07.663165Z" + "iopub.execute_input": "2023-08-22T06:59:11.475355Z", + "iopub.status.busy": "2023-08-22T06:59:11.475181Z", + "iopub.status.idle": "2023-08-22T06:59:11.696662Z", + "shell.execute_reply": "2023-08-22T06:59:11.696345Z" } }, "outputs": [ @@ -2209,10 +2209,10 @@ "id": "01019508", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:07.665255Z", - "iopub.status.busy": "2023-08-21T03:36:07.665128Z", - "iopub.status.idle": "2023-08-21T03:36:07.766569Z", - "shell.execute_reply": "2023-08-21T03:36:07.766244Z" + "iopub.execute_input": "2023-08-22T06:59:11.698588Z", + "iopub.status.busy": "2023-08-22T06:59:11.698446Z", + "iopub.status.idle": "2023-08-22T06:59:11.986555Z", + "shell.execute_reply": "2023-08-22T06:59:11.986111Z" }, "lines_to_next_cell": 0 }, @@ -2250,10 +2250,10 @@ "id": "7d08992f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:07.768244Z", - "iopub.status.busy": "2023-08-21T03:36:07.768130Z", - "iopub.status.idle": "2023-08-21T03:36:07.893027Z", - "shell.execute_reply": "2023-08-21T03:36:07.892660Z" + "iopub.execute_input": "2023-08-22T06:59:11.988767Z", + "iopub.status.busy": "2023-08-22T06:59:11.988614Z", + "iopub.status.idle": "2023-08-22T06:59:12.104022Z", + "shell.execute_reply": "2023-08-22T06:59:12.103708Z" }, "lines_to_next_cell": 0 }, @@ -2296,10 +2296,10 @@ "id": "1f89d704", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:07.894802Z", - "iopub.status.busy": "2023-08-21T03:36:07.894695Z", - "iopub.status.idle": "2023-08-21T03:36:07.995202Z", - "shell.execute_reply": "2023-08-21T03:36:07.994863Z" + "iopub.execute_input": "2023-08-22T06:59:12.105969Z", + "iopub.status.busy": "2023-08-22T06:59:12.105871Z", + "iopub.status.idle": "2023-08-22T06:59:12.198374Z", + "shell.execute_reply": "2023-08-22T06:59:12.197945Z" }, "lines_to_next_cell": 2 }, @@ -2344,10 +2344,10 @@ "id": "cd971131", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:07.996961Z", - "iopub.status.busy": "2023-08-21T03:36:07.996836Z", - "iopub.status.idle": "2023-08-21T03:36:07.999447Z", - "shell.execute_reply": "2023-08-21T03:36:07.999154Z" + "iopub.execute_input": "2023-08-22T06:59:12.200827Z", + "iopub.status.busy": "2023-08-22T06:59:12.200655Z", + "iopub.status.idle": "2023-08-22T06:59:12.203494Z", + "shell.execute_reply": "2023-08-22T06:59:12.203195Z" }, "lines_to_next_cell": 2 }, @@ -2384,10 +2384,10 @@ "id": "aa630d16", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.001028Z", - "iopub.status.busy": "2023-08-21T03:36:08.000925Z", - "iopub.status.idle": "2023-08-21T03:36:08.003185Z", - "shell.execute_reply": "2023-08-21T03:36:08.002908Z" + "iopub.execute_input": "2023-08-22T06:59:12.205484Z", + "iopub.status.busy": "2023-08-22T06:59:12.205351Z", + "iopub.status.idle": "2023-08-22T06:59:12.207885Z", + "shell.execute_reply": "2023-08-22T06:59:12.207564Z" } }, "outputs": [ @@ -2425,10 +2425,10 @@ "id": "89955ee2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.004703Z", - "iopub.status.busy": "2023-08-21T03:36:08.004594Z", - "iopub.status.idle": "2023-08-21T03:36:08.006692Z", - "shell.execute_reply": "2023-08-21T03:36:08.006429Z" + "iopub.execute_input": "2023-08-22T06:59:12.209574Z", + "iopub.status.busy": "2023-08-22T06:59:12.209454Z", + "iopub.status.idle": "2023-08-22T06:59:12.212189Z", + "shell.execute_reply": "2023-08-22T06:59:12.211670Z" }, "lines_to_next_cell": 0 }, @@ -2463,10 +2463,10 @@ "id": "517f592d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.008158Z", - "iopub.status.busy": "2023-08-21T03:36:08.008062Z", - "iopub.status.idle": "2023-08-21T03:36:08.010083Z", - "shell.execute_reply": "2023-08-21T03:36:08.009819Z" + "iopub.execute_input": "2023-08-22T06:59:12.214090Z", + "iopub.status.busy": "2023-08-22T06:59:12.213954Z", + "iopub.status.idle": "2023-08-22T06:59:12.216634Z", + "shell.execute_reply": "2023-08-22T06:59:12.216229Z" } }, "outputs": [ @@ -2530,10 +2530,10 @@ "id": "35927abd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.011518Z", - "iopub.status.busy": "2023-08-21T03:36:08.011425Z", - "iopub.status.idle": "2023-08-21T03:36:08.013558Z", - "shell.execute_reply": "2023-08-21T03:36:08.013317Z" + "iopub.execute_input": "2023-08-22T06:59:12.218701Z", + "iopub.status.busy": "2023-08-22T06:59:12.218551Z", + "iopub.status.idle": "2023-08-22T06:59:12.221779Z", + "shell.execute_reply": "2023-08-22T06:59:12.221078Z" } }, "outputs": [ @@ -2571,10 +2571,10 @@ "id": "78ee7f5b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.014962Z", - "iopub.status.busy": "2023-08-21T03:36:08.014871Z", - "iopub.status.idle": "2023-08-21T03:36:08.016938Z", - "shell.execute_reply": "2023-08-21T03:36:08.016689Z" + "iopub.execute_input": "2023-08-22T06:59:12.223849Z", + "iopub.status.busy": "2023-08-22T06:59:12.223730Z", + "iopub.status.idle": "2023-08-22T06:59:12.226375Z", + "shell.execute_reply": "2023-08-22T06:59:12.226073Z" } }, "outputs": [ @@ -2612,10 +2612,10 @@ "id": "16212696", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.018506Z", - "iopub.status.busy": "2023-08-21T03:36:08.018407Z", - "iopub.status.idle": "2023-08-21T03:36:08.020572Z", - "shell.execute_reply": "2023-08-21T03:36:08.020301Z" + "iopub.execute_input": "2023-08-22T06:59:12.228118Z", + "iopub.status.busy": "2023-08-22T06:59:12.227993Z", + "iopub.status.idle": "2023-08-22T06:59:12.230254Z", + "shell.execute_reply": "2023-08-22T06:59:12.229993Z" } }, "outputs": [ @@ -2651,10 +2651,10 @@ "id": "d5f473d2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.022048Z", - "iopub.status.busy": "2023-08-21T03:36:08.021947Z", - "iopub.status.idle": "2023-08-21T03:36:08.024301Z", - "shell.execute_reply": "2023-08-21T03:36:08.024006Z" + "iopub.execute_input": "2023-08-22T06:59:12.231967Z", + "iopub.status.busy": "2023-08-22T06:59:12.231858Z", + "iopub.status.idle": "2023-08-22T06:59:12.234317Z", + "shell.execute_reply": "2023-08-22T06:59:12.234032Z" } }, "outputs": [ @@ -2692,10 +2692,10 @@ "id": "c89646d6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.025864Z", - "iopub.status.busy": "2023-08-21T03:36:08.025745Z", - "iopub.status.idle": "2023-08-21T03:36:08.027990Z", - "shell.execute_reply": "2023-08-21T03:36:08.027692Z" + "iopub.execute_input": "2023-08-22T06:59:12.236331Z", + "iopub.status.busy": "2023-08-22T06:59:12.236194Z", + "iopub.status.idle": "2023-08-22T06:59:12.238788Z", + "shell.execute_reply": "2023-08-22T06:59:12.238448Z" } }, "outputs": [ @@ -2728,10 +2728,10 @@ "id": "87f6b4f2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.029541Z", - "iopub.status.busy": "2023-08-21T03:36:08.029439Z", - "iopub.status.idle": "2023-08-21T03:36:08.031650Z", - "shell.execute_reply": "2023-08-21T03:36:08.031378Z" + "iopub.execute_input": "2023-08-22T06:59:12.240562Z", + "iopub.status.busy": "2023-08-22T06:59:12.240434Z", + "iopub.status.idle": "2023-08-22T06:59:12.242956Z", + "shell.execute_reply": "2023-08-22T06:59:12.242696Z" } }, "outputs": [ @@ -2764,10 +2764,10 @@ "id": "5da5bda8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.033127Z", - "iopub.status.busy": "2023-08-21T03:36:08.033016Z", - "iopub.status.idle": "2023-08-21T03:36:08.056275Z", - "shell.execute_reply": "2023-08-21T03:36:08.056014Z" + "iopub.execute_input": "2023-08-22T06:59:12.244576Z", + "iopub.status.busy": "2023-08-22T06:59:12.244457Z", + "iopub.status.idle": "2023-08-22T06:59:12.267725Z", + "shell.execute_reply": "2023-08-22T06:59:12.267329Z" } }, "outputs": [ @@ -2803,10 +2803,10 @@ "id": "ac48a95b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.057926Z", - "iopub.status.busy": "2023-08-21T03:36:08.057815Z", - "iopub.status.idle": "2023-08-21T03:36:08.060231Z", - "shell.execute_reply": "2023-08-21T03:36:08.059980Z" + "iopub.execute_input": "2023-08-22T06:59:12.269754Z", + "iopub.status.busy": "2023-08-22T06:59:12.269614Z", + "iopub.status.idle": "2023-08-22T06:59:12.272265Z", + "shell.execute_reply": "2023-08-22T06:59:12.271962Z" }, "lines_to_next_cell": 0 }, @@ -2852,10 +2852,10 @@ "id": "ee195cc4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.061798Z", - "iopub.status.busy": "2023-08-21T03:36:08.061686Z", - "iopub.status.idle": "2023-08-21T03:36:08.063979Z", - "shell.execute_reply": "2023-08-21T03:36:08.063715Z" + "iopub.execute_input": "2023-08-22T06:59:12.273998Z", + "iopub.status.busy": "2023-08-22T06:59:12.273857Z", + "iopub.status.idle": "2023-08-22T06:59:12.276565Z", + "shell.execute_reply": "2023-08-22T06:59:12.276235Z" }, "lines_to_next_cell": 2 }, @@ -2895,10 +2895,10 @@ "id": "48917bb5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.067067Z", - "iopub.status.busy": "2023-08-21T03:36:08.066955Z", - "iopub.status.idle": "2023-08-21T03:36:08.069279Z", - "shell.execute_reply": "2023-08-21T03:36:08.068966Z" + "iopub.execute_input": "2023-08-22T06:59:12.278649Z", + "iopub.status.busy": "2023-08-22T06:59:12.278494Z", + "iopub.status.idle": "2023-08-22T06:59:12.281280Z", + "shell.execute_reply": "2023-08-22T06:59:12.280843Z" }, "lines_to_next_cell": 0 }, @@ -2965,10 +2965,10 @@ "id": "5d4caf22", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.070903Z", - "iopub.status.busy": "2023-08-21T03:36:08.070799Z", - "iopub.status.idle": "2023-08-21T03:36:08.072959Z", - "shell.execute_reply": "2023-08-21T03:36:08.072701Z" + "iopub.execute_input": "2023-08-22T06:59:12.282968Z", + "iopub.status.busy": "2023-08-22T06:59:12.282854Z", + "iopub.status.idle": "2023-08-22T06:59:12.285234Z", + "shell.execute_reply": "2023-08-22T06:59:12.284961Z" }, "lines_to_next_cell": 0 }, @@ -3003,10 +3003,10 @@ "id": "348820e3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.074422Z", - "iopub.status.busy": "2023-08-21T03:36:08.074322Z", - "iopub.status.idle": "2023-08-21T03:36:08.076398Z", - "shell.execute_reply": "2023-08-21T03:36:08.076106Z" + "iopub.execute_input": "2023-08-22T06:59:12.287427Z", + "iopub.status.busy": "2023-08-22T06:59:12.287184Z", + "iopub.status.idle": "2023-08-22T06:59:12.289915Z", + "shell.execute_reply": "2023-08-22T06:59:12.289589Z" } }, "outputs": [ @@ -3042,10 +3042,10 @@ "id": "4aafe45b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.077952Z", - "iopub.status.busy": "2023-08-21T03:36:08.077853Z", - "iopub.status.idle": "2023-08-21T03:36:08.080163Z", - "shell.execute_reply": "2023-08-21T03:36:08.079901Z" + "iopub.execute_input": "2023-08-22T06:59:12.291767Z", + "iopub.status.busy": "2023-08-22T06:59:12.291634Z", + "iopub.status.idle": "2023-08-22T06:59:12.294302Z", + "shell.execute_reply": "2023-08-22T06:59:12.293967Z" } }, "outputs": [ @@ -3088,10 +3088,10 @@ "id": "1be6a588", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.081637Z", - "iopub.status.busy": "2023-08-21T03:36:08.081537Z", - "iopub.status.idle": "2023-08-21T03:36:08.083796Z", - "shell.execute_reply": "2023-08-21T03:36:08.083505Z" + "iopub.execute_input": "2023-08-22T06:59:12.296189Z", + "iopub.status.busy": "2023-08-22T06:59:12.296052Z", + "iopub.status.idle": "2023-08-22T06:59:12.298784Z", + "shell.execute_reply": "2023-08-22T06:59:12.298383Z" } }, "outputs": [ @@ -3127,10 +3127,10 @@ "id": "e83da57b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.085319Z", - "iopub.status.busy": "2023-08-21T03:36:08.085216Z", - "iopub.status.idle": "2023-08-21T03:36:08.087359Z", - "shell.execute_reply": "2023-08-21T03:36:08.087095Z" + "iopub.execute_input": "2023-08-22T06:59:12.300802Z", + "iopub.status.busy": "2023-08-22T06:59:12.300663Z", + "iopub.status.idle": "2023-08-22T06:59:12.303240Z", + "shell.execute_reply": "2023-08-22T06:59:12.302865Z" } }, "outputs": [ @@ -3174,10 +3174,10 @@ "id": "09675294", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.088904Z", - "iopub.status.busy": "2023-08-21T03:36:08.088804Z", - "iopub.status.idle": "2023-08-21T03:36:08.091266Z", - "shell.execute_reply": "2023-08-21T03:36:08.090985Z" + "iopub.execute_input": "2023-08-22T06:59:12.305143Z", + "iopub.status.busy": "2023-08-22T06:59:12.305025Z", + "iopub.status.idle": "2023-08-22T06:59:12.307529Z", + "shell.execute_reply": "2023-08-22T06:59:12.307219Z" } }, "outputs": [ @@ -3214,10 +3214,10 @@ "id": "a85614e4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.092757Z", - "iopub.status.busy": "2023-08-21T03:36:08.092664Z", - "iopub.status.idle": "2023-08-21T03:36:08.094873Z", - "shell.execute_reply": "2023-08-21T03:36:08.094581Z" + "iopub.execute_input": "2023-08-22T06:59:12.309554Z", + "iopub.status.busy": "2023-08-22T06:59:12.309418Z", + "iopub.status.idle": "2023-08-22T06:59:12.311737Z", + "shell.execute_reply": "2023-08-22T06:59:12.311441Z" }, "lines_to_next_cell": 0 }, @@ -3306,10 +3306,10 @@ "id": "ff81e644", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.096546Z", - "iopub.status.busy": "2023-08-21T03:36:08.096449Z", - "iopub.status.idle": "2023-08-21T03:36:08.317557Z", - "shell.execute_reply": "2023-08-21T03:36:08.317278Z" + "iopub.execute_input": "2023-08-22T06:59:12.313549Z", + "iopub.status.busy": "2023-08-22T06:59:12.313428Z", + "iopub.status.idle": "2023-08-22T06:59:14.571524Z", + "shell.execute_reply": "2023-08-22T06:59:14.571235Z" } }, "outputs": [ @@ -3538,10 +3538,10 @@ "id": "5b45aa7f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.319194Z", - "iopub.status.busy": "2023-08-21T03:36:08.319082Z", - "iopub.status.idle": "2023-08-21T03:36:08.322312Z", - "shell.execute_reply": "2023-08-21T03:36:08.322063Z" + "iopub.execute_input": "2023-08-22T06:59:14.573445Z", + "iopub.status.busy": "2023-08-22T06:59:14.573227Z", + "iopub.status.idle": "2023-08-22T06:59:14.576905Z", + "shell.execute_reply": "2023-08-22T06:59:14.576624Z" }, "lines_to_next_cell": 0 }, @@ -3575,10 +3575,10 @@ "id": "413f626a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.323776Z", - "iopub.status.busy": "2023-08-21T03:36:08.323686Z", - "iopub.status.idle": "2023-08-21T03:36:08.326230Z", - "shell.execute_reply": "2023-08-21T03:36:08.325989Z" + "iopub.execute_input": "2023-08-22T06:59:14.578771Z", + "iopub.status.busy": "2023-08-22T06:59:14.578606Z", + "iopub.status.idle": "2023-08-22T06:59:14.581727Z", + "shell.execute_reply": "2023-08-22T06:59:14.581462Z" }, "lines_to_next_cell": 0 }, @@ -3626,10 +3626,10 @@ "id": "57b86346", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.327630Z", - "iopub.status.busy": "2023-08-21T03:36:08.327527Z", - "iopub.status.idle": "2023-08-21T03:36:08.329871Z", - "shell.execute_reply": "2023-08-21T03:36:08.329620Z" + "iopub.execute_input": "2023-08-22T06:59:14.583287Z", + "iopub.status.busy": "2023-08-22T06:59:14.583182Z", + "iopub.status.idle": "2023-08-22T06:59:14.585660Z", + "shell.execute_reply": "2023-08-22T06:59:14.585401Z" }, "lines_to_next_cell": 0 }, @@ -3687,10 +3687,10 @@ "id": "a9698b26", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.331328Z", - "iopub.status.busy": "2023-08-21T03:36:08.331230Z", - "iopub.status.idle": "2023-08-21T03:36:08.334947Z", - "shell.execute_reply": "2023-08-21T03:36:08.334715Z" + "iopub.execute_input": "2023-08-22T06:59:14.587247Z", + "iopub.status.busy": "2023-08-22T06:59:14.587134Z", + "iopub.status.idle": "2023-08-22T06:59:14.591564Z", + "shell.execute_reply": "2023-08-22T06:59:14.591275Z" }, "lines_to_next_cell": 2 }, @@ -3728,10 +3728,10 @@ "id": "4877cb2c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.336400Z", - "iopub.status.busy": "2023-08-21T03:36:08.336322Z", - "iopub.status.idle": "2023-08-21T03:36:08.338340Z", - "shell.execute_reply": "2023-08-21T03:36:08.338101Z" + "iopub.execute_input": "2023-08-22T06:59:14.593387Z", + "iopub.status.busy": "2023-08-22T06:59:14.593233Z", + "iopub.status.idle": "2023-08-22T06:59:14.595399Z", + "shell.execute_reply": "2023-08-22T06:59:14.595111Z" } }, "outputs": [ @@ -3767,10 +3767,10 @@ "id": "2ba1d33d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.339714Z", - "iopub.status.busy": "2023-08-21T03:36:08.339640Z", - "iopub.status.idle": "2023-08-21T03:36:08.342116Z", - "shell.execute_reply": "2023-08-21T03:36:08.341883Z" + "iopub.execute_input": "2023-08-22T06:59:14.596985Z", + "iopub.status.busy": "2023-08-22T06:59:14.596880Z", + "iopub.status.idle": "2023-08-22T06:59:14.599760Z", + "shell.execute_reply": "2023-08-22T06:59:14.599435Z" }, "lines_to_next_cell": 2 }, @@ -3807,10 +3807,10 @@ "id": "3d03baab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.343548Z", - "iopub.status.busy": "2023-08-21T03:36:08.343471Z", - "iopub.status.idle": "2023-08-21T03:36:08.345627Z", - "shell.execute_reply": "2023-08-21T03:36:08.345379Z" + "iopub.execute_input": "2023-08-22T06:59:14.602110Z", + "iopub.status.busy": "2023-08-22T06:59:14.601973Z", + "iopub.status.idle": "2023-08-22T06:59:14.604410Z", + "shell.execute_reply": "2023-08-22T06:59:14.604070Z" }, "lines_to_next_cell": 2 }, @@ -3851,10 +3851,10 @@ "id": "410b4dd7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.347031Z", - "iopub.status.busy": "2023-08-21T03:36:08.346955Z", - "iopub.status.idle": "2023-08-21T03:36:08.351303Z", - "shell.execute_reply": "2023-08-21T03:36:08.351081Z" + "iopub.execute_input": "2023-08-22T06:59:14.606098Z", + "iopub.status.busy": "2023-08-22T06:59:14.605987Z", + "iopub.status.idle": "2023-08-22T06:59:14.610632Z", + "shell.execute_reply": "2023-08-22T06:59:14.610234Z" }, "lines_to_next_cell": 0 }, @@ -3967,10 +3967,10 @@ "id": "3540804d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.352671Z", - "iopub.status.busy": "2023-08-21T03:36:08.352588Z", - "iopub.status.idle": "2023-08-21T03:36:08.365290Z", - "shell.execute_reply": "2023-08-21T03:36:08.365017Z" + "iopub.execute_input": "2023-08-22T06:59:14.612402Z", + "iopub.status.busy": "2023-08-22T06:59:14.612271Z", + "iopub.status.idle": "2023-08-22T06:59:14.625177Z", + "shell.execute_reply": "2023-08-22T06:59:14.624839Z" }, "lines_to_next_cell": 0 }, @@ -4854,10 +4854,10 @@ "id": "66d174f1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.366746Z", - "iopub.status.busy": "2023-08-21T03:36:08.366648Z", - "iopub.status.idle": "2023-08-21T03:36:08.371142Z", - "shell.execute_reply": "2023-08-21T03:36:08.370889Z" + "iopub.execute_input": "2023-08-22T06:59:14.626829Z", + "iopub.status.busy": "2023-08-22T06:59:14.626719Z", + "iopub.status.idle": "2023-08-22T06:59:14.631422Z", + "shell.execute_reply": "2023-08-22T06:59:14.631104Z" }, "lines_to_next_cell": 0 }, @@ -4989,10 +4989,10 @@ "id": "52789c77", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.372548Z", - "iopub.status.busy": "2023-08-21T03:36:08.372468Z", - "iopub.status.idle": "2023-08-21T03:36:08.374676Z", - "shell.execute_reply": "2023-08-21T03:36:08.374427Z" + "iopub.execute_input": "2023-08-22T06:59:14.633211Z", + "iopub.status.busy": "2023-08-22T06:59:14.633074Z", + "iopub.status.idle": "2023-08-22T06:59:14.635511Z", + "shell.execute_reply": "2023-08-22T06:59:14.635190Z" }, "lines_to_next_cell": 0 }, @@ -5030,10 +5030,10 @@ "id": "d83650bf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.376096Z", - "iopub.status.busy": "2023-08-21T03:36:08.376016Z", - "iopub.status.idle": "2023-08-21T03:36:08.382023Z", - "shell.execute_reply": "2023-08-21T03:36:08.381780Z" + "iopub.execute_input": "2023-08-22T06:59:14.637090Z", + "iopub.status.busy": "2023-08-22T06:59:14.636984Z", + "iopub.status.idle": "2023-08-22T06:59:14.643250Z", + "shell.execute_reply": "2023-08-22T06:59:14.642921Z" } }, "outputs": [ @@ -5254,10 +5254,10 @@ "id": "880d79d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.383342Z", - "iopub.status.busy": "2023-08-21T03:36:08.383266Z", - "iopub.status.idle": "2023-08-21T03:36:08.385348Z", - "shell.execute_reply": "2023-08-21T03:36:08.385111Z" + "iopub.execute_input": "2023-08-22T06:59:14.644959Z", + "iopub.status.busy": "2023-08-22T06:59:14.644832Z", + "iopub.status.idle": "2023-08-22T06:59:14.647067Z", + "shell.execute_reply": "2023-08-22T06:59:14.646780Z" }, "lines_to_next_cell": 0 }, @@ -5297,10 +5297,10 @@ "id": "c01f4095", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.386800Z", - "iopub.status.busy": "2023-08-21T03:36:08.386719Z", - "iopub.status.idle": "2023-08-21T03:36:08.390900Z", - "shell.execute_reply": "2023-08-21T03:36:08.390675Z" + "iopub.execute_input": "2023-08-22T06:59:14.648616Z", + "iopub.status.busy": "2023-08-22T06:59:14.648506Z", + "iopub.status.idle": "2023-08-22T06:59:14.653020Z", + "shell.execute_reply": "2023-08-22T06:59:14.652673Z" }, "lines_to_next_cell": 0 }, @@ -5410,10 +5410,10 @@ "id": "a4202eb8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.392405Z", - "iopub.status.busy": "2023-08-21T03:36:08.392322Z", - "iopub.status.idle": "2023-08-21T03:36:08.396509Z", - "shell.execute_reply": "2023-08-21T03:36:08.396244Z" + "iopub.execute_input": "2023-08-22T06:59:14.654821Z", + "iopub.status.busy": "2023-08-22T06:59:14.654708Z", + "iopub.status.idle": "2023-08-22T06:59:14.658952Z", + "shell.execute_reply": "2023-08-22T06:59:14.658676Z" }, "lines_to_next_cell": 0 }, @@ -5522,10 +5522,10 @@ "id": "948b2d07", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.397917Z", - "iopub.status.busy": "2023-08-21T03:36:08.397838Z", - "iopub.status.idle": "2023-08-21T03:36:08.402447Z", - "shell.execute_reply": "2023-08-21T03:36:08.402192Z" + "iopub.execute_input": "2023-08-22T06:59:14.660688Z", + "iopub.status.busy": "2023-08-22T06:59:14.660582Z", + "iopub.status.idle": "2023-08-22T06:59:14.665683Z", + "shell.execute_reply": "2023-08-22T06:59:14.665320Z" }, "lines_to_next_cell": 0 }, @@ -5676,10 +5676,10 @@ "id": "1cfdcc5c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.403773Z", - "iopub.status.busy": "2023-08-21T03:36:08.403692Z", - "iopub.status.idle": "2023-08-21T03:36:08.407036Z", - "shell.execute_reply": "2023-08-21T03:36:08.406794Z" + "iopub.execute_input": "2023-08-22T06:59:14.667792Z", + "iopub.status.busy": "2023-08-22T06:59:14.667649Z", + "iopub.status.idle": "2023-08-22T06:59:14.672086Z", + "shell.execute_reply": "2023-08-22T06:59:14.671668Z" }, "lines_to_next_cell": 0 }, @@ -5763,10 +5763,10 @@ "id": "fd9c5cda", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.408461Z", - "iopub.status.busy": "2023-08-21T03:36:08.408387Z", - "iopub.status.idle": "2023-08-21T03:36:08.411673Z", - "shell.execute_reply": "2023-08-21T03:36:08.411429Z" + "iopub.execute_input": "2023-08-22T06:59:14.674155Z", + "iopub.status.busy": "2023-08-22T06:59:14.674020Z", + "iopub.status.idle": "2023-08-22T06:59:14.677900Z", + "shell.execute_reply": "2023-08-22T06:59:14.677570Z" }, "lines_to_next_cell": 0 }, @@ -5856,10 +5856,10 @@ "id": "6d431cb5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.413092Z", - "iopub.status.busy": "2023-08-21T03:36:08.413016Z", - "iopub.status.idle": "2023-08-21T03:36:08.418535Z", - "shell.execute_reply": "2023-08-21T03:36:08.418303Z" + "iopub.execute_input": "2023-08-22T06:59:14.679838Z", + "iopub.status.busy": "2023-08-22T06:59:14.679694Z", + "iopub.status.idle": "2023-08-22T06:59:14.685743Z", + "shell.execute_reply": "2023-08-22T06:59:14.685309Z" }, "lines_to_next_cell": 2 }, @@ -6276,10 +6276,10 @@ "id": "fac41ce1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.419882Z", - "iopub.status.busy": "2023-08-21T03:36:08.419806Z", - "iopub.status.idle": "2023-08-21T03:36:08.425319Z", - "shell.execute_reply": "2023-08-21T03:36:08.425073Z" + "iopub.execute_input": "2023-08-22T06:59:14.687552Z", + "iopub.status.busy": "2023-08-22T06:59:14.687421Z", + "iopub.status.idle": "2023-08-22T06:59:14.693477Z", + "shell.execute_reply": "2023-08-22T06:59:14.693157Z" }, "lines_to_next_cell": 0 }, @@ -6700,10 +6700,10 @@ "id": "b0885654", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.426701Z", - "iopub.status.busy": "2023-08-21T03:36:08.426621Z", - "iopub.status.idle": "2023-08-21T03:36:08.431378Z", - "shell.execute_reply": "2023-08-21T03:36:08.431148Z" + "iopub.execute_input": "2023-08-22T06:59:14.695271Z", + "iopub.status.busy": "2023-08-22T06:59:14.695116Z", + "iopub.status.idle": "2023-08-22T06:59:14.700210Z", + "shell.execute_reply": "2023-08-22T06:59:14.699919Z" }, "lines_to_next_cell": 0 }, @@ -6980,10 +6980,10 @@ "id": "213945a6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.432780Z", - "iopub.status.busy": "2023-08-21T03:36:08.432696Z", - "iopub.status.idle": "2023-08-21T03:36:08.438473Z", - "shell.execute_reply": "2023-08-21T03:36:08.438250Z" + "iopub.execute_input": "2023-08-22T06:59:14.702075Z", + "iopub.status.busy": "2023-08-22T06:59:14.701942Z", + "iopub.status.idle": "2023-08-22T06:59:14.708849Z", + "shell.execute_reply": "2023-08-22T06:59:14.708468Z" }, "lines_to_next_cell": 0 }, @@ -7371,10 +7371,10 @@ "id": "a3c4060a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.439902Z", - "iopub.status.busy": "2023-08-21T03:36:08.439825Z", - "iopub.status.idle": "2023-08-21T03:36:08.441707Z", - "shell.execute_reply": "2023-08-21T03:36:08.441464Z" + "iopub.execute_input": "2023-08-22T06:59:14.710560Z", + "iopub.status.busy": "2023-08-22T06:59:14.710418Z", + "iopub.status.idle": "2023-08-22T06:59:14.712662Z", + "shell.execute_reply": "2023-08-22T06:59:14.712379Z" }, "lines_to_next_cell": 0 }, @@ -7415,10 +7415,10 @@ "id": "f2bffb69", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.443349Z", - "iopub.status.busy": "2023-08-21T03:36:08.443241Z", - "iopub.status.idle": "2023-08-21T03:36:08.445173Z", - "shell.execute_reply": "2023-08-21T03:36:08.444935Z" + "iopub.execute_input": "2023-08-22T06:59:14.714548Z", + "iopub.status.busy": "2023-08-22T06:59:14.714416Z", + "iopub.status.idle": "2023-08-22T06:59:14.716724Z", + "shell.execute_reply": "2023-08-22T06:59:14.716275Z" }, "lines_to_next_cell": 0 }, @@ -7467,10 +7467,10 @@ "id": "ee827a53", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.446610Z", - "iopub.status.busy": "2023-08-21T03:36:08.446520Z", - "iopub.status.idle": "2023-08-21T03:36:08.448323Z", - "shell.execute_reply": "2023-08-21T03:36:08.448070Z" + "iopub.execute_input": "2023-08-22T06:59:14.718538Z", + "iopub.status.busy": "2023-08-22T06:59:14.718418Z", + "iopub.status.idle": "2023-08-22T06:59:14.720778Z", + "shell.execute_reply": "2023-08-22T06:59:14.720410Z" } }, "outputs": [ @@ -7522,10 +7522,10 @@ "id": "3a097fbc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.449714Z", - "iopub.status.busy": "2023-08-21T03:36:08.449623Z", - "iopub.status.idle": "2023-08-21T03:36:08.454097Z", - "shell.execute_reply": "2023-08-21T03:36:08.453813Z" + "iopub.execute_input": "2023-08-22T06:59:14.722745Z", + "iopub.status.busy": "2023-08-22T06:59:14.722445Z", + "iopub.status.idle": "2023-08-22T06:59:14.727643Z", + "shell.execute_reply": "2023-08-22T06:59:14.727302Z" }, "lines_to_next_cell": 2 }, @@ -7618,10 +7618,10 @@ "id": "e064e170", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.455528Z", - "iopub.status.busy": "2023-08-21T03:36:08.455447Z", - "iopub.status.idle": "2023-08-21T03:36:08.457783Z", - "shell.execute_reply": "2023-08-21T03:36:08.457543Z" + "iopub.execute_input": "2023-08-22T06:59:14.729283Z", + "iopub.status.busy": "2023-08-22T06:59:14.729148Z", + "iopub.status.idle": "2023-08-22T06:59:14.732019Z", + "shell.execute_reply": "2023-08-22T06:59:14.731599Z" }, "lines_to_next_cell": 0 }, @@ -7675,10 +7675,10 @@ "id": "c915ca52", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.459248Z", - "iopub.status.busy": "2023-08-21T03:36:08.459155Z", - "iopub.status.idle": "2023-08-21T03:36:08.552220Z", - "shell.execute_reply": "2023-08-21T03:36:08.551914Z" + "iopub.execute_input": "2023-08-22T06:59:14.733916Z", + "iopub.status.busy": "2023-08-22T06:59:14.733787Z", + "iopub.status.idle": "2023-08-22T06:59:14.823121Z", + "shell.execute_reply": "2023-08-22T06:59:14.822788Z" }, "lines_to_next_cell": 0 }, @@ -7724,10 +7724,10 @@ "id": "65cd6d02", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.553877Z", - "iopub.status.busy": "2023-08-21T03:36:08.553770Z", - "iopub.status.idle": "2023-08-21T03:36:08.640715Z", - "shell.execute_reply": "2023-08-21T03:36:08.640411Z" + "iopub.execute_input": "2023-08-22T06:59:14.825251Z", + "iopub.status.busy": "2023-08-22T06:59:14.825113Z", + "iopub.status.idle": "2023-08-22T06:59:14.905789Z", + "shell.execute_reply": "2023-08-22T06:59:14.905480Z" }, "lines_to_next_cell": 0 }, @@ -7766,10 +7766,10 @@ "id": "76b5c0b1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.642311Z", - "iopub.status.busy": "2023-08-21T03:36:08.642208Z", - "iopub.status.idle": "2023-08-21T03:36:08.737860Z", - "shell.execute_reply": "2023-08-21T03:36:08.737565Z" + "iopub.execute_input": "2023-08-22T06:59:14.907636Z", + "iopub.status.busy": "2023-08-22T06:59:14.907511Z", + "iopub.status.idle": "2023-08-22T06:59:14.997060Z", + "shell.execute_reply": "2023-08-22T06:59:14.996751Z" }, "lines_to_next_cell": 0 }, @@ -7806,10 +7806,10 @@ "id": "183a2c2b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.739490Z", - "iopub.status.busy": "2023-08-21T03:36:08.739374Z", - "iopub.status.idle": "2023-08-21T03:36:08.779690Z", - "shell.execute_reply": "2023-08-21T03:36:08.779420Z" + "iopub.execute_input": "2023-08-22T06:59:14.998844Z", + "iopub.status.busy": "2023-08-22T06:59:14.998722Z", + "iopub.status.idle": "2023-08-22T06:59:15.033680Z", + "shell.execute_reply": "2023-08-22T06:59:15.033292Z" } }, "outputs": [], @@ -7837,10 +7837,10 @@ "id": "75fbb981", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.781309Z", - "iopub.status.busy": "2023-08-21T03:36:08.781225Z", - "iopub.status.idle": "2023-08-21T03:36:08.949186Z", - "shell.execute_reply": "2023-08-21T03:36:08.948871Z" + "iopub.execute_input": "2023-08-22T06:59:15.035870Z", + "iopub.status.busy": "2023-08-22T06:59:15.035723Z", + "iopub.status.idle": "2023-08-22T06:59:15.198431Z", + "shell.execute_reply": "2023-08-22T06:59:15.198047Z" } }, "outputs": [ @@ -7885,10 +7885,10 @@ "id": "55b3a1cc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.950967Z", - "iopub.status.busy": "2023-08-21T03:36:08.950852Z", - "iopub.status.idle": "2023-08-21T03:36:08.953871Z", - "shell.execute_reply": "2023-08-21T03:36:08.953602Z" + "iopub.execute_input": "2023-08-22T06:59:15.200753Z", + "iopub.status.busy": "2023-08-22T06:59:15.200526Z", + "iopub.status.idle": "2023-08-22T06:59:15.203898Z", + "shell.execute_reply": "2023-08-22T06:59:15.203556Z" }, "lines_to_next_cell": 0 }, @@ -7924,10 +7924,10 @@ "id": "f3d88794", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:08.955394Z", - "iopub.status.busy": "2023-08-21T03:36:08.955297Z", - "iopub.status.idle": "2023-08-21T03:36:09.060746Z", - "shell.execute_reply": "2023-08-21T03:36:09.060425Z" + "iopub.execute_input": "2023-08-22T06:59:15.205746Z", + "iopub.status.busy": "2023-08-22T06:59:15.205652Z", + "iopub.status.idle": "2023-08-22T06:59:15.305674Z", + "shell.execute_reply": "2023-08-22T06:59:15.305240Z" } }, "outputs": [ @@ -7961,10 +7961,10 @@ "id": "eea49f5b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:09.062393Z", - "iopub.status.busy": "2023-08-21T03:36:09.062272Z", - "iopub.status.idle": "2023-08-21T03:36:09.164120Z", - "shell.execute_reply": "2023-08-21T03:36:09.163816Z" + "iopub.execute_input": "2023-08-22T06:59:15.307641Z", + "iopub.status.busy": "2023-08-22T06:59:15.307491Z", + "iopub.status.idle": "2023-08-22T06:59:15.405217Z", + "shell.execute_reply": "2023-08-22T06:59:15.404785Z" }, "lines_to_next_cell": 0 }, @@ -7999,10 +7999,10 @@ "id": "d5bcfff8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:09.165860Z", - "iopub.status.busy": "2023-08-21T03:36:09.165745Z", - "iopub.status.idle": "2023-08-21T03:36:09.262378Z", - "shell.execute_reply": "2023-08-21T03:36:09.262095Z" + "iopub.execute_input": "2023-08-22T06:59:15.407321Z", + "iopub.status.busy": "2023-08-22T06:59:15.407190Z", + "iopub.status.idle": "2023-08-22T06:59:15.503021Z", + "shell.execute_reply": "2023-08-22T06:59:15.502607Z" }, "lines_to_next_cell": 0 }, @@ -8041,10 +8041,10 @@ "id": "edb66cae", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:09.264050Z", - "iopub.status.busy": "2023-08-21T03:36:09.263936Z", - "iopub.status.idle": "2023-08-21T03:36:10.182139Z", - "shell.execute_reply": "2023-08-21T03:36:10.181842Z" + "iopub.execute_input": "2023-08-22T06:59:15.504880Z", + "iopub.status.busy": "2023-08-22T06:59:15.504746Z", + "iopub.status.idle": "2023-08-22T06:59:16.457367Z", + "shell.execute_reply": "2023-08-22T06:59:16.457033Z" }, "lines_to_next_cell": 0 }, @@ -8079,10 +8079,10 @@ "id": "4f5d25d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:10.183868Z", - "iopub.status.busy": "2023-08-21T03:36:10.183754Z", - "iopub.status.idle": "2023-08-21T03:36:10.401318Z", - "shell.execute_reply": "2023-08-21T03:36:10.401033Z" + "iopub.execute_input": "2023-08-22T06:59:16.459211Z", + "iopub.status.busy": "2023-08-22T06:59:16.459087Z", + "iopub.status.idle": "2023-08-22T06:59:16.684825Z", + "shell.execute_reply": "2023-08-22T06:59:16.684501Z" }, "lines_to_next_cell": 0 }, @@ -8118,10 +8118,10 @@ "id": "ce7b23e2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:10.403067Z", - "iopub.status.busy": "2023-08-21T03:36:10.402953Z", - "iopub.status.idle": "2023-08-21T03:36:10.408957Z", - "shell.execute_reply": "2023-08-21T03:36:10.408683Z" + "iopub.execute_input": "2023-08-22T06:59:16.686592Z", + "iopub.status.busy": "2023-08-22T06:59:16.686475Z", + "iopub.status.idle": "2023-08-22T06:59:16.692382Z", + "shell.execute_reply": "2023-08-22T06:59:16.692013Z" }, "lines_to_next_cell": 0 }, @@ -8231,10 +8231,10 @@ "id": "a6545d2f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T03:36:10.410500Z", - "iopub.status.busy": "2023-08-21T03:36:10.410393Z", - "iopub.status.idle": "2023-08-21T03:36:10.414627Z", - "shell.execute_reply": "2023-08-21T03:36:10.414350Z" + "iopub.execute_input": "2023-08-22T06:59:16.694320Z", + "iopub.status.busy": "2023-08-22T06:59:16.694191Z", + "iopub.status.idle": "2023-08-22T06:59:16.698368Z", + "shell.execute_reply": "2023-08-22T06:59:16.698098Z" }, "lines_to_next_cell": 0 }, @@ -8278,7 +8278,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -8291,7 +8291,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch03-linreg-lab.Rmd b/Ch03-linreg-lab.Rmd index 930176c..afb1570 100644 --- a/Ch03-linreg-lab.Rmd +++ b/Ch03-linreg-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch03-linreg-lab.ipynb b/Ch03-linreg-lab.ipynb index f039622..78a516a 100644 --- a/Ch03-linreg-lab.ipynb +++ b/Ch03-linreg-lab.ipynb @@ -27,10 +27,10 @@ "id": "b18c1628", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:52.978537Z", - "iopub.status.busy": "2023-08-21T02:28:52.978403Z", - "iopub.status.idle": "2023-08-21T02:28:53.456488Z", - "shell.execute_reply": "2023-08-21T02:28:53.456172Z" + "iopub.execute_input": "2023-08-22T06:59:19.963803Z", + "iopub.status.busy": "2023-08-22T06:59:19.963695Z", + "iopub.status.idle": "2023-08-22T06:59:20.384511Z", + "shell.execute_reply": "2023-08-22T06:59:20.384219Z" }, "lines_to_next_cell": 2 }, @@ -60,10 +60,10 @@ "id": "a10a1d7d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:53.458574Z", - "iopub.status.busy": "2023-08-21T02:28:53.458356Z", - "iopub.status.idle": "2023-08-21T02:28:54.074306Z", - "shell.execute_reply": "2023-08-21T02:28:54.073950Z" + "iopub.execute_input": "2023-08-22T06:59:20.387117Z", + "iopub.status.busy": "2023-08-22T06:59:20.386932Z", + "iopub.status.idle": "2023-08-22T06:59:27.510372Z", + "shell.execute_reply": "2023-08-22T06:59:27.510076Z" }, "lines_to_next_cell": 0 }, @@ -93,10 +93,10 @@ "id": "756c0524", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.076206Z", - "iopub.status.busy": "2023-08-21T02:28:54.076055Z", - "iopub.status.idle": "2023-08-21T02:28:54.079277Z", - "shell.execute_reply": "2023-08-21T02:28:54.079022Z" + "iopub.execute_input": "2023-08-22T06:59:27.512280Z", + "iopub.status.busy": "2023-08-22T06:59:27.512129Z", + "iopub.status.idle": "2023-08-22T06:59:27.514850Z", + "shell.execute_reply": "2023-08-22T06:59:27.514531Z" } }, "outputs": [], @@ -124,10 +124,10 @@ "id": "2c370a6e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.080812Z", - "iopub.status.busy": "2023-08-21T02:28:54.080717Z", - "iopub.status.idle": "2023-08-21T02:28:54.212489Z", - "shell.execute_reply": "2023-08-21T02:28:54.212195Z" + "iopub.execute_input": "2023-08-22T06:59:27.516720Z", + "iopub.status.busy": "2023-08-22T06:59:27.516584Z", + "iopub.status.idle": "2023-08-22T06:59:30.213761Z", + "shell.execute_reply": "2023-08-22T06:59:30.213403Z" } }, "outputs": [], @@ -156,10 +156,10 @@ "id": "43bcb3a9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.214288Z", - "iopub.status.busy": "2023-08-21T02:28:54.214131Z", - "iopub.status.idle": "2023-08-21T02:28:54.218521Z", - "shell.execute_reply": "2023-08-21T02:28:54.217256Z" + "iopub.execute_input": "2023-08-22T06:59:30.215847Z", + "iopub.status.busy": "2023-08-22T06:59:30.215679Z", + "iopub.status.idle": "2023-08-22T06:59:30.219200Z", + "shell.execute_reply": "2023-08-22T06:59:30.218854Z" }, "lines_to_next_cell": 0 }, @@ -237,10 +237,10 @@ "id": "cb81bfb0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.228152Z", - "iopub.status.busy": "2023-08-21T02:28:54.227968Z", - "iopub.status.idle": "2023-08-21T02:28:54.231888Z", - "shell.execute_reply": "2023-08-21T02:28:54.231418Z" + "iopub.execute_input": "2023-08-22T06:59:30.220842Z", + "iopub.status.busy": "2023-08-22T06:59:30.220745Z", + "iopub.status.idle": "2023-08-22T06:59:30.223794Z", + "shell.execute_reply": "2023-08-22T06:59:30.223517Z" }, "lines_to_next_cell": 0 }, @@ -282,7 +282,6 @@ " '__ge__',\n", " '__getattribute__',\n", " '__getitem__',\n", - " '__getstate__',\n", " '__gt__',\n", " '__hash__',\n", " '__iadd__',\n", @@ -441,10 +440,10 @@ "id": "29e9bdab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.233644Z", - "iopub.status.busy": "2023-08-21T02:28:54.233538Z", - "iopub.status.idle": "2023-08-21T02:28:54.236077Z", - "shell.execute_reply": "2023-08-21T02:28:54.235804Z" + "iopub.execute_input": "2023-08-22T06:59:30.225458Z", + "iopub.status.busy": "2023-08-22T06:59:30.225336Z", + "iopub.status.idle": "2023-08-22T06:59:30.227667Z", + "shell.execute_reply": "2023-08-22T06:59:30.227385Z" }, "lines_to_next_cell": 0 }, @@ -499,10 +498,10 @@ "id": "72a15de5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.237580Z", - "iopub.status.busy": "2023-08-21T02:28:54.237492Z", - "iopub.status.idle": "2023-08-21T02:28:54.242855Z", - "shell.execute_reply": "2023-08-21T02:28:54.242574Z" + "iopub.execute_input": "2023-08-22T06:59:30.229351Z", + "iopub.status.busy": "2023-08-22T06:59:30.229229Z", + "iopub.status.idle": "2023-08-22T06:59:30.233978Z", + "shell.execute_reply": "2023-08-22T06:59:30.233738Z" } }, "outputs": [ @@ -543,10 +542,10 @@ "id": "26837cfe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.244219Z", - "iopub.status.busy": "2023-08-21T02:28:54.244127Z", - "iopub.status.idle": "2023-08-21T02:28:54.249714Z", - "shell.execute_reply": "2023-08-21T02:28:54.249432Z" + "iopub.execute_input": "2023-08-22T06:59:30.235692Z", + "iopub.status.busy": "2023-08-22T06:59:30.235572Z", + "iopub.status.idle": "2023-08-22T06:59:30.240548Z", + "shell.execute_reply": "2023-08-22T06:59:30.240254Z" } }, "outputs": [ @@ -633,10 +632,10 @@ "id": "c70cbdb5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.251263Z", - "iopub.status.busy": "2023-08-21T02:28:54.251136Z", - "iopub.status.idle": "2023-08-21T02:28:54.253671Z", - "shell.execute_reply": "2023-08-21T02:28:54.253397Z" + "iopub.execute_input": "2023-08-22T06:59:30.242180Z", + "iopub.status.busy": "2023-08-22T06:59:30.242082Z", + "iopub.status.idle": "2023-08-22T06:59:30.244423Z", + "shell.execute_reply": "2023-08-22T06:59:30.244043Z" }, "lines_to_next_cell": 0 }, @@ -668,10 +667,10 @@ "id": "6f2d7c78", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.255193Z", - "iopub.status.busy": "2023-08-21T02:28:54.255089Z", - "iopub.status.idle": "2023-08-21T02:28:54.314793Z", - "shell.execute_reply": "2023-08-21T02:28:54.314504Z" + "iopub.execute_input": "2023-08-22T06:59:30.246386Z", + "iopub.status.busy": "2023-08-22T06:59:30.246241Z", + "iopub.status.idle": "2023-08-22T06:59:30.304643Z", + "shell.execute_reply": "2023-08-22T06:59:30.304342Z" }, "lines_to_next_cell": 2 }, @@ -779,10 +778,10 @@ "id": "cdec4294", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.316521Z", - "iopub.status.busy": "2023-08-21T02:28:54.316363Z", - "iopub.status.idle": "2023-08-21T02:28:54.323224Z", - "shell.execute_reply": "2023-08-21T02:28:54.322970Z" + "iopub.execute_input": "2023-08-22T06:59:30.306725Z", + "iopub.status.busy": "2023-08-22T06:59:30.306537Z", + "iopub.status.idle": "2023-08-22T06:59:30.313928Z", + "shell.execute_reply": "2023-08-22T06:59:30.313612Z" }, "lines_to_next_cell": 0 }, @@ -874,10 +873,10 @@ "id": "edf2efcb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.324750Z", - "iopub.status.busy": "2023-08-21T02:28:54.324644Z", - "iopub.status.idle": "2023-08-21T02:28:54.330945Z", - "shell.execute_reply": "2023-08-21T02:28:54.330680Z" + "iopub.execute_input": "2023-08-22T06:59:30.316046Z", + "iopub.status.busy": "2023-08-22T06:59:30.315927Z", + "iopub.status.idle": "2023-08-22T06:59:30.322463Z", + "shell.execute_reply": "2023-08-22T06:59:30.322137Z" }, "lines_to_next_cell": 0 }, @@ -978,10 +977,10 @@ "id": "49fc8992", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.332402Z", - "iopub.status.busy": "2023-08-21T02:28:54.332319Z", - "iopub.status.idle": "2023-08-21T02:28:54.341724Z", - "shell.execute_reply": "2023-08-21T02:28:54.341409Z" + "iopub.execute_input": "2023-08-22T06:59:30.324453Z", + "iopub.status.busy": "2023-08-22T06:59:30.324311Z", + "iopub.status.idle": "2023-08-22T06:59:30.334219Z", + "shell.execute_reply": "2023-08-22T06:59:30.333881Z" } }, "outputs": [ @@ -1000,10 +999,10 @@ " Method: Least Squares F-statistic: 601.6\n", "\n", "\n", - " Date: Sun, 20 Aug 2023 Prob (F-statistic): 5.08e-88\n", + " Date: Mon, 21 Aug 2023 Prob (F-statistic): 5.08e-88\n", "\n", "\n", - " Time: 19:28:54 Log-Likelihood: -1641.5\n", + " Time: 23:59:30 Log-Likelihood: -1641.5\n", "\n", "\n", " No. Observations: 506 AIC: 3287.\n", @@ -1051,8 +1050,8 @@ "\\textbf{Dep. Variable:} & medv & \\textbf{ R-squared: } & 0.544 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.543 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 601.6 \\\\\n", - "\\textbf{Date:} & Sun, 20 Aug 2023 & \\textbf{ Prob (F-statistic):} & 5.08e-88 \\\\\n", - "\\textbf{Time:} & 19:28:54 & \\textbf{ Log-Likelihood: } & -1641.5 \\\\\n", + "\\textbf{Date:} & Mon, 21 Aug 2023 & \\textbf{ Prob (F-statistic):} & 5.08e-88 \\\\\n", + "\\textbf{Time:} & 23:59:30 & \\textbf{ Log-Likelihood: } & -1641.5 \\\\\n", "\\textbf{No. Observations:} & 506 & \\textbf{ AIC: } & 3287. \\\\\n", "\\textbf{Df Residuals:} & 504 & \\textbf{ BIC: } & 3295. \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -1087,8 +1086,8 @@ "Dep. Variable: medv R-squared: 0.544\n", "Model: OLS Adj. R-squared: 0.543\n", "Method: Least Squares F-statistic: 601.6\n", - "Date: Sun, 20 Aug 2023 Prob (F-statistic): 5.08e-88\n", - "Time: 19:28:54 Log-Likelihood: -1641.5\n", + "Date: Mon, 21 Aug 2023 Prob (F-statistic): 5.08e-88\n", + "Time: 23:59:30 Log-Likelihood: -1641.5\n", "No. Observations: 506 AIC: 3287.\n", "Df Residuals: 504 BIC: 3295.\n", "Df Model: 1 \n", @@ -1134,10 +1133,10 @@ "id": "6d0f4c3a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.343317Z", - "iopub.status.busy": "2023-08-21T02:28:54.343206Z", - "iopub.status.idle": "2023-08-21T02:28:54.345604Z", - "shell.execute_reply": "2023-08-21T02:28:54.345339Z" + "iopub.execute_input": "2023-08-22T06:59:30.336071Z", + "iopub.status.busy": "2023-08-22T06:59:30.335936Z", + "iopub.status.idle": "2023-08-22T06:59:30.338769Z", + "shell.execute_reply": "2023-08-22T06:59:30.338416Z" }, "lines_to_next_cell": 2 }, @@ -1177,10 +1176,10 @@ "id": "132ffded", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.347097Z", - "iopub.status.busy": "2023-08-21T02:28:54.346984Z", - "iopub.status.idle": "2023-08-21T02:28:54.350802Z", - "shell.execute_reply": "2023-08-21T02:28:54.350563Z" + "iopub.execute_input": "2023-08-22T06:59:30.340661Z", + "iopub.status.busy": "2023-08-22T06:59:30.340521Z", + "iopub.status.idle": "2023-08-22T06:59:30.345095Z", + "shell.execute_reply": "2023-08-22T06:59:30.344737Z" } }, "outputs": [ @@ -1261,10 +1260,10 @@ "id": "b654a050", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.352369Z", - "iopub.status.busy": "2023-08-21T02:28:54.352275Z", - "iopub.status.idle": "2023-08-21T02:28:54.354743Z", - "shell.execute_reply": "2023-08-21T02:28:54.354502Z" + "iopub.execute_input": "2023-08-22T06:59:30.346874Z", + "iopub.status.busy": "2023-08-22T06:59:30.346735Z", + "iopub.status.idle": "2023-08-22T06:59:30.349291Z", + "shell.execute_reply": "2023-08-22T06:59:30.349007Z" }, "lines_to_next_cell": 0 }, @@ -1299,10 +1298,10 @@ "id": "148ed59d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.356140Z", - "iopub.status.busy": "2023-08-21T02:28:54.356063Z", - "iopub.status.idle": "2023-08-21T02:28:54.358315Z", - "shell.execute_reply": "2023-08-21T02:28:54.358059Z" + "iopub.execute_input": "2023-08-22T06:59:30.351073Z", + "iopub.status.busy": "2023-08-22T06:59:30.350934Z", + "iopub.status.idle": "2023-08-22T06:59:30.353516Z", + "shell.execute_reply": "2023-08-22T06:59:30.353225Z" }, "lines_to_next_cell": 0 }, @@ -1338,10 +1337,10 @@ "id": "d9d7b844", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.359757Z", - "iopub.status.busy": "2023-08-21T02:28:54.359678Z", - "iopub.status.idle": "2023-08-21T02:28:54.362165Z", - "shell.execute_reply": "2023-08-21T02:28:54.361908Z" + "iopub.execute_input": "2023-08-22T06:59:30.355404Z", + "iopub.status.busy": "2023-08-22T06:59:30.355267Z", + "iopub.status.idle": "2023-08-22T06:59:30.357759Z", + "shell.execute_reply": "2023-08-22T06:59:30.357461Z" }, "lines_to_next_cell": 0 }, @@ -1398,10 +1397,10 @@ "id": "79de2913", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.363741Z", - "iopub.status.busy": "2023-08-21T02:28:54.363634Z", - "iopub.status.idle": "2023-08-21T02:28:54.365531Z", - "shell.execute_reply": "2023-08-21T02:28:54.365292Z" + "iopub.execute_input": "2023-08-22T06:59:30.359458Z", + "iopub.status.busy": "2023-08-22T06:59:30.359349Z", + "iopub.status.idle": "2023-08-22T06:59:30.361244Z", + "shell.execute_reply": "2023-08-22T06:59:30.360996Z" }, "lines_to_next_cell": 0 }, @@ -1432,10 +1431,10 @@ "id": "a9b843c7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.366874Z", - "iopub.status.busy": "2023-08-21T02:28:54.366784Z", - "iopub.status.idle": "2023-08-21T02:28:54.368713Z", - "shell.execute_reply": "2023-08-21T02:28:54.368464Z" + "iopub.execute_input": "2023-08-22T06:59:30.362847Z", + "iopub.status.busy": "2023-08-22T06:59:30.362727Z", + "iopub.status.idle": "2023-08-22T06:59:30.364665Z", + "shell.execute_reply": "2023-08-22T06:59:30.364410Z" }, "lines_to_next_cell": 0 }, @@ -1472,10 +1471,10 @@ "id": "7e116800", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.370163Z", - "iopub.status.busy": "2023-08-21T02:28:54.370073Z", - "iopub.status.idle": "2023-08-21T02:28:54.466696Z", - "shell.execute_reply": "2023-08-21T02:28:54.466338Z" + "iopub.execute_input": "2023-08-22T06:59:30.366228Z", + "iopub.status.busy": "2023-08-22T06:59:30.366005Z", + "iopub.status.idle": "2023-08-22T06:59:30.451927Z", + "shell.execute_reply": "2023-08-22T06:59:30.451614Z" }, "lines_to_next_cell": 0 }, @@ -1536,10 +1535,10 @@ "id": "b524399e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.468561Z", - "iopub.status.busy": "2023-08-21T02:28:54.468356Z", - "iopub.status.idle": "2023-08-21T02:28:54.566644Z", - "shell.execute_reply": "2023-08-21T02:28:54.566337Z" + "iopub.execute_input": "2023-08-22T06:59:30.453712Z", + "iopub.status.busy": "2023-08-22T06:59:30.453582Z", + "iopub.status.idle": "2023-08-22T06:59:30.544088Z", + "shell.execute_reply": "2023-08-22T06:59:30.543647Z" }, "lines_to_next_cell": 0 }, @@ -1584,10 +1583,10 @@ "id": "8c95b6b0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.568423Z", - "iopub.status.busy": "2023-08-21T02:28:54.568307Z", - "iopub.status.idle": "2023-08-21T02:28:54.658553Z", - "shell.execute_reply": "2023-08-21T02:28:54.658267Z" + "iopub.execute_input": "2023-08-22T06:59:30.545979Z", + "iopub.status.busy": "2023-08-22T06:59:30.545829Z", + "iopub.status.idle": "2023-08-22T06:59:30.629333Z", + "shell.execute_reply": "2023-08-22T06:59:30.628907Z" }, "lines_to_next_cell": 0 }, @@ -1652,10 +1651,10 @@ "id": "04ed8362", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.660197Z", - "iopub.status.busy": "2023-08-21T02:28:54.660075Z", - "iopub.status.idle": "2023-08-21T02:28:54.672237Z", - "shell.execute_reply": "2023-08-21T02:28:54.671994Z" + "iopub.execute_input": "2023-08-22T06:59:30.631304Z", + "iopub.status.busy": "2023-08-22T06:59:30.631172Z", + "iopub.status.idle": "2023-08-22T06:59:30.643510Z", + "shell.execute_reply": "2023-08-22T06:59:30.643228Z" }, "lines_to_next_cell": 0 }, @@ -1750,10 +1749,10 @@ "id": "6483e190", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.673721Z", - "iopub.status.busy": "2023-08-21T02:28:54.673628Z", - "iopub.status.idle": "2023-08-21T02:28:54.676304Z", - "shell.execute_reply": "2023-08-21T02:28:54.675993Z" + "iopub.execute_input": "2023-08-22T06:59:30.645260Z", + "iopub.status.busy": "2023-08-22T06:59:30.645134Z", + "iopub.status.idle": "2023-08-22T06:59:30.647637Z", + "shell.execute_reply": "2023-08-22T06:59:30.647310Z" } }, "outputs": [ @@ -1790,10 +1789,10 @@ "id": "99a6f9d0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.677828Z", - "iopub.status.busy": "2023-08-21T02:28:54.677709Z", - "iopub.status.idle": "2023-08-21T02:28:54.696704Z", - "shell.execute_reply": "2023-08-21T02:28:54.696381Z" + "iopub.execute_input": "2023-08-22T06:59:30.649333Z", + "iopub.status.busy": "2023-08-22T06:59:30.649188Z", + "iopub.status.idle": "2023-08-22T06:59:30.667501Z", + "shell.execute_reply": "2023-08-22T06:59:30.667201Z" } }, "outputs": [ @@ -1966,10 +1965,10 @@ "id": "78cf55d0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.698261Z", - "iopub.status.busy": "2023-08-21T02:28:54.698177Z", - "iopub.status.idle": "2023-08-21T02:28:54.716362Z", - "shell.execute_reply": "2023-08-21T02:28:54.716082Z" + "iopub.execute_input": "2023-08-22T06:59:30.669290Z", + "iopub.status.busy": "2023-08-22T06:59:30.669145Z", + "iopub.status.idle": "2023-08-22T06:59:30.687452Z", + "shell.execute_reply": "2023-08-22T06:59:30.687132Z" } }, "outputs": [ @@ -2154,10 +2153,10 @@ "id": "902f6474", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.718076Z", - "iopub.status.busy": "2023-08-21T02:28:54.717951Z", - "iopub.status.idle": "2023-08-21T02:28:54.725146Z", - "shell.execute_reply": "2023-08-21T02:28:54.724859Z" + "iopub.execute_input": "2023-08-22T06:59:30.689227Z", + "iopub.status.busy": "2023-08-22T06:59:30.689072Z", + "iopub.status.idle": "2023-08-22T06:59:30.695976Z", + "shell.execute_reply": "2023-08-22T06:59:30.695604Z" }, "lines_to_next_cell": 0 }, @@ -2286,10 +2285,10 @@ "id": "ea1c88e9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.726658Z", - "iopub.status.busy": "2023-08-21T02:28:54.726565Z", - "iopub.status.idle": "2023-08-21T02:28:54.731850Z", - "shell.execute_reply": "2023-08-21T02:28:54.731595Z" + "iopub.execute_input": "2023-08-22T06:59:30.697875Z", + "iopub.status.busy": "2023-08-22T06:59:30.697744Z", + "iopub.status.idle": "2023-08-22T06:59:30.702854Z", + "shell.execute_reply": "2023-08-22T06:59:30.702396Z" }, "lines_to_next_cell": 0 }, @@ -2319,10 +2318,10 @@ "id": "e9ff159c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.733388Z", - "iopub.status.busy": "2023-08-21T02:28:54.733294Z", - "iopub.status.idle": "2023-08-21T02:28:54.747346Z", - "shell.execute_reply": "2023-08-21T02:28:54.747067Z" + "iopub.execute_input": "2023-08-22T06:59:30.705060Z", + "iopub.status.busy": "2023-08-22T06:59:30.704928Z", + "iopub.status.idle": "2023-08-22T06:59:30.718546Z", + "shell.execute_reply": "2023-08-22T06:59:30.718288Z" }, "lines_to_next_cell": 2 }, @@ -2427,10 +2426,10 @@ "id": "c98f54b2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.748856Z", - "iopub.status.busy": "2023-08-21T02:28:54.748774Z", - "iopub.status.idle": "2023-08-21T02:28:54.763149Z", - "shell.execute_reply": "2023-08-21T02:28:54.762853Z" + "iopub.execute_input": "2023-08-22T06:59:30.720315Z", + "iopub.status.busy": "2023-08-22T06:59:30.720166Z", + "iopub.status.idle": "2023-08-22T06:59:30.733544Z", + "shell.execute_reply": "2023-08-22T06:59:30.733105Z" }, "lines_to_next_cell": 0 }, @@ -2545,10 +2544,10 @@ "id": "53065cac", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.764924Z", - "iopub.status.busy": "2023-08-21T02:28:54.764796Z", - "iopub.status.idle": "2023-08-21T02:28:54.770846Z", - "shell.execute_reply": "2023-08-21T02:28:54.770578Z" + "iopub.execute_input": "2023-08-22T06:59:30.735632Z", + "iopub.status.busy": "2023-08-22T06:59:30.735509Z", + "iopub.status.idle": "2023-08-22T06:59:30.740862Z", + "shell.execute_reply": "2023-08-22T06:59:30.740579Z" }, "lines_to_next_cell": 0 }, @@ -2655,10 +2654,10 @@ "id": "8c654809", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.772408Z", - "iopub.status.busy": "2023-08-21T02:28:54.772296Z", - "iopub.status.idle": "2023-08-21T02:28:54.872389Z", - "shell.execute_reply": "2023-08-21T02:28:54.872086Z" + "iopub.execute_input": "2023-08-22T06:59:30.742508Z", + "iopub.status.busy": "2023-08-22T06:59:30.742387Z", + "iopub.status.idle": "2023-08-22T06:59:30.835769Z", + "shell.execute_reply": "2023-08-22T06:59:30.835368Z" }, "lines_to_next_cell": 0 }, @@ -2666,7 +2665,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 34, @@ -2721,10 +2720,10 @@ "id": "2182f0ec", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.874000Z", - "iopub.status.busy": "2023-08-21T02:28:54.873893Z", - "iopub.status.idle": "2023-08-21T02:28:54.879085Z", - "shell.execute_reply": "2023-08-21T02:28:54.878817Z" + "iopub.execute_input": "2023-08-22T06:59:30.837767Z", + "iopub.status.busy": "2023-08-22T06:59:30.837636Z", + "iopub.status.idle": "2023-08-22T06:59:30.842380Z", + "shell.execute_reply": "2023-08-22T06:59:30.842026Z" }, "lines_to_next_cell": 0 }, @@ -2772,10 +2771,10 @@ "id": "d614fdcb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:54.880583Z", - "iopub.status.busy": "2023-08-21T02:28:54.880499Z", - "iopub.status.idle": "2023-08-21T02:28:54.907341Z", - "shell.execute_reply": "2023-08-21T02:28:54.907079Z" + "iopub.execute_input": "2023-08-22T06:59:30.844518Z", + "iopub.status.busy": "2023-08-22T06:59:30.844367Z", + "iopub.status.idle": "2023-08-22T06:59:30.871405Z", + "shell.execute_reply": "2023-08-22T06:59:30.871052Z" }, "lines_to_next_cell": 0 }, @@ -2967,7 +2966,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -2980,7 +2979,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch04-classification-lab.Rmd b/Ch04-classification-lab.Rmd index d3d8eec..de81f30 100644 --- a/Ch04-classification-lab.Rmd +++ b/Ch04-classification-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch04-classification-lab.ipynb b/Ch04-classification-lab.ipynb index 0ab61a0..eaa27c9 100644 --- a/Ch04-classification-lab.ipynb +++ b/Ch04-classification-lab.ipynb @@ -46,10 +46,10 @@ "id": "95d28c33", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:56.229892Z", - "iopub.status.busy": "2023-08-21T02:28:56.229672Z", - "iopub.status.idle": "2023-08-21T02:28:57.250402Z", - "shell.execute_reply": "2023-08-21T02:28:57.249963Z" + "iopub.execute_input": "2023-08-22T06:59:34.104356Z", + "iopub.status.busy": "2023-08-22T06:59:34.104253Z", + "iopub.status.idle": "2023-08-22T06:59:35.054094Z", + "shell.execute_reply": "2023-08-22T06:59:35.053754Z" } }, "outputs": [], @@ -77,10 +77,10 @@ "id": "f7fb5f2a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.252390Z", - "iopub.status.busy": "2023-08-21T02:28:57.252224Z", - "iopub.status.idle": "2023-08-21T02:28:57.272851Z", - "shell.execute_reply": "2023-08-21T02:28:57.272588Z" + "iopub.execute_input": "2023-08-22T06:59:35.056108Z", + "iopub.status.busy": "2023-08-22T06:59:35.055950Z", + "iopub.status.idle": "2023-08-22T06:59:35.206682Z", + "shell.execute_reply": "2023-08-22T06:59:35.206381Z" }, "lines_to_next_cell": 2 }, @@ -112,10 +112,10 @@ "id": "7845390b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.274355Z", - "iopub.status.busy": "2023-08-21T02:28:57.274258Z", - "iopub.status.idle": "2023-08-21T02:28:57.285160Z", - "shell.execute_reply": "2023-08-21T02:28:57.284894Z" + "iopub.execute_input": "2023-08-22T06:59:35.208609Z", + "iopub.status.busy": "2023-08-22T06:59:35.208499Z", + "iopub.status.idle": "2023-08-22T06:59:35.218856Z", + "shell.execute_reply": "2023-08-22T06:59:35.218543Z" }, "lines_to_next_cell": 0 }, @@ -332,10 +332,10 @@ "id": "a92e287a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.286715Z", - "iopub.status.busy": "2023-08-21T02:28:57.286616Z", - "iopub.status.idle": "2023-08-21T02:28:57.288883Z", - "shell.execute_reply": "2023-08-21T02:28:57.288648Z" + "iopub.execute_input": "2023-08-22T06:59:35.220822Z", + "iopub.status.busy": "2023-08-22T06:59:35.220658Z", + "iopub.status.idle": "2023-08-22T06:59:35.223146Z", + "shell.execute_reply": "2023-08-22T06:59:35.222864Z" }, "lines_to_next_cell": 0 }, @@ -376,10 +376,10 @@ "id": "96bb1e00", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.290357Z", - "iopub.status.busy": "2023-08-21T02:28:57.290259Z", - "iopub.status.idle": "2023-08-21T02:28:57.295352Z", - "shell.execute_reply": "2023-08-21T02:28:57.295083Z" + "iopub.execute_input": "2023-08-22T06:59:35.224965Z", + "iopub.status.busy": "2023-08-22T06:59:35.224831Z", + "iopub.status.idle": "2023-08-22T06:59:35.229701Z", + "shell.execute_reply": "2023-08-22T06:59:35.229442Z" }, "lines_to_next_cell": 0 }, @@ -557,10 +557,10 @@ "id": "4ddb96ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.296784Z", - "iopub.status.busy": "2023-08-21T02:28:57.296683Z", - "iopub.status.idle": "2023-08-21T02:28:57.392956Z", - "shell.execute_reply": "2023-08-21T02:28:57.392526Z" + "iopub.execute_input": "2023-08-22T06:59:35.231485Z", + "iopub.status.busy": "2023-08-22T06:59:35.231354Z", + "iopub.status.idle": "2023-08-22T06:59:35.319629Z", + "shell.execute_reply": "2023-08-22T06:59:35.319199Z" }, "lines_to_next_cell": 2 }, @@ -604,10 +604,10 @@ "id": "df59bcac", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.394870Z", - "iopub.status.busy": "2023-08-21T02:28:57.394721Z", - "iopub.status.idle": "2023-08-21T02:28:57.461746Z", - "shell.execute_reply": "2023-08-21T02:28:57.461390Z" + "iopub.execute_input": "2023-08-22T06:59:35.321733Z", + "iopub.status.busy": "2023-08-22T06:59:35.321597Z", + "iopub.status.idle": "2023-08-22T06:59:35.382350Z", + "shell.execute_reply": "2023-08-22T06:59:35.381919Z" }, "lines_to_next_cell": 0 }, @@ -744,10 +744,10 @@ "id": "f45f26de", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.463719Z", - "iopub.status.busy": "2023-08-21T02:28:57.463515Z", - "iopub.status.idle": "2023-08-21T02:28:57.466512Z", - "shell.execute_reply": "2023-08-21T02:28:57.466227Z" + "iopub.execute_input": "2023-08-22T06:59:35.384570Z", + "iopub.status.busy": "2023-08-22T06:59:35.384386Z", + "iopub.status.idle": "2023-08-22T06:59:35.387199Z", + "shell.execute_reply": "2023-08-22T06:59:35.386864Z" }, "lines_to_next_cell": 0 }, @@ -789,10 +789,10 @@ "id": "e9f38895", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.468254Z", - "iopub.status.busy": "2023-08-21T02:28:57.468113Z", - "iopub.status.idle": "2023-08-21T02:28:57.470717Z", - "shell.execute_reply": "2023-08-21T02:28:57.470403Z" + "iopub.execute_input": "2023-08-22T06:59:35.388777Z", + "iopub.status.busy": "2023-08-22T06:59:35.388675Z", + "iopub.status.idle": "2023-08-22T06:59:35.391038Z", + "shell.execute_reply": "2023-08-22T06:59:35.390743Z" } }, "outputs": [ @@ -840,10 +840,10 @@ "id": "4f20356d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.472719Z", - "iopub.status.busy": "2023-08-21T02:28:57.472578Z", - "iopub.status.idle": "2023-08-21T02:28:57.475369Z", - "shell.execute_reply": "2023-08-21T02:28:57.475043Z" + "iopub.execute_input": "2023-08-22T06:59:35.392617Z", + "iopub.status.busy": "2023-08-22T06:59:35.392475Z", + "iopub.status.idle": "2023-08-22T06:59:35.395311Z", + "shell.execute_reply": "2023-08-22T06:59:35.395042Z" }, "lines_to_next_cell": 0 }, @@ -884,10 +884,10 @@ "id": "152b3063", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.477166Z", - "iopub.status.busy": "2023-08-21T02:28:57.477052Z", - "iopub.status.idle": "2023-08-21T02:28:57.479161Z", - "shell.execute_reply": "2023-08-21T02:28:57.478803Z" + "iopub.execute_input": "2023-08-22T06:59:35.396940Z", + "iopub.status.busy": "2023-08-22T06:59:35.396826Z", + "iopub.status.idle": "2023-08-22T06:59:35.398854Z", + "shell.execute_reply": "2023-08-22T06:59:35.398548Z" }, "lines_to_next_cell": 0 }, @@ -917,10 +917,10 @@ "id": "0f89f7ae", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.480867Z", - "iopub.status.busy": "2023-08-21T02:28:57.480737Z", - "iopub.status.idle": "2023-08-21T02:28:57.487133Z", - "shell.execute_reply": "2023-08-21T02:28:57.486778Z" + "iopub.execute_input": "2023-08-22T06:59:35.400476Z", + "iopub.status.busy": "2023-08-22T06:59:35.400370Z", + "iopub.status.idle": "2023-08-22T06:59:35.406304Z", + "shell.execute_reply": "2023-08-22T06:59:35.405994Z" } }, "outputs": [ @@ -1006,10 +1006,10 @@ "id": "d55dd7ec", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.488873Z", - "iopub.status.busy": "2023-08-21T02:28:57.488756Z", - "iopub.status.idle": "2023-08-21T02:28:57.491458Z", - "shell.execute_reply": "2023-08-21T02:28:57.491162Z" + "iopub.execute_input": "2023-08-22T06:59:35.408001Z", + "iopub.status.busy": "2023-08-22T06:59:35.407888Z", + "iopub.status.idle": "2023-08-22T06:59:35.410520Z", + "shell.execute_reply": "2023-08-22T06:59:35.410222Z" }, "lines_to_next_cell": 0 }, @@ -1069,10 +1069,10 @@ "id": "b998a060", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.493306Z", - "iopub.status.busy": "2023-08-21T02:28:57.493174Z", - "iopub.status.idle": "2023-08-21T02:28:57.496464Z", - "shell.execute_reply": "2023-08-21T02:28:57.496107Z" + "iopub.execute_input": "2023-08-22T06:59:35.412348Z", + "iopub.status.busy": "2023-08-22T06:59:35.412200Z", + "iopub.status.idle": "2023-08-22T06:59:35.415670Z", + "shell.execute_reply": "2023-08-22T06:59:35.415360Z" }, "lines_to_next_cell": 2 }, @@ -1135,10 +1135,10 @@ "id": "814e34ce", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.498135Z", - "iopub.status.busy": "2023-08-21T02:28:57.498043Z", - "iopub.status.idle": "2023-08-21T02:28:57.502996Z", - "shell.execute_reply": "2023-08-21T02:28:57.502676Z" + "iopub.execute_input": "2023-08-22T06:59:35.417692Z", + "iopub.status.busy": "2023-08-22T06:59:35.417600Z", + "iopub.status.idle": "2023-08-22T06:59:35.422315Z", + "shell.execute_reply": "2023-08-22T06:59:35.421995Z" } }, "outputs": [], @@ -1172,10 +1172,10 @@ "id": "644823f9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.504874Z", - "iopub.status.busy": "2023-08-21T02:28:57.504743Z", - "iopub.status.idle": "2023-08-21T02:28:57.506879Z", - "shell.execute_reply": "2023-08-21T02:28:57.506558Z" + "iopub.execute_input": "2023-08-22T06:59:35.424241Z", + "iopub.status.busy": "2023-08-22T06:59:35.424099Z", + "iopub.status.idle": "2023-08-22T06:59:35.426371Z", + "shell.execute_reply": "2023-08-22T06:59:35.425999Z" }, "lines_to_next_cell": 0 }, @@ -1201,10 +1201,10 @@ "id": "51217c85", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.508502Z", - "iopub.status.busy": "2023-08-21T02:28:57.508403Z", - "iopub.status.idle": "2023-08-21T02:28:57.512973Z", - "shell.execute_reply": "2023-08-21T02:28:57.512720Z" + "iopub.execute_input": "2023-08-22T06:59:35.428375Z", + "iopub.status.busy": "2023-08-22T06:59:35.428239Z", + "iopub.status.idle": "2023-08-22T06:59:35.432849Z", + "shell.execute_reply": "2023-08-22T06:59:35.432491Z" }, "lines_to_next_cell": 0 }, @@ -1286,10 +1286,10 @@ "id": "a73446bf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.514459Z", - "iopub.status.busy": "2023-08-21T02:28:57.514377Z", - "iopub.status.idle": "2023-08-21T02:28:57.517011Z", - "shell.execute_reply": "2023-08-21T02:28:57.516758Z" + "iopub.execute_input": "2023-08-22T06:59:35.434489Z", + "iopub.status.busy": "2023-08-22T06:59:35.434371Z", + "iopub.status.idle": "2023-08-22T06:59:35.437237Z", + "shell.execute_reply": "2023-08-22T06:59:35.436914Z" }, "lines_to_next_cell": 2 }, @@ -1344,10 +1344,10 @@ "id": "8174767d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.518464Z", - "iopub.status.busy": "2023-08-21T02:28:57.518363Z", - "iopub.status.idle": "2023-08-21T02:28:57.528289Z", - "shell.execute_reply": "2023-08-21T02:28:57.527983Z" + "iopub.execute_input": "2023-08-22T06:59:35.439129Z", + "iopub.status.busy": "2023-08-22T06:59:35.438980Z", + "iopub.status.idle": "2023-08-22T06:59:35.449036Z", + "shell.execute_reply": "2023-08-22T06:59:35.448757Z" }, "lines_to_next_cell": 2 }, @@ -1438,10 +1438,10 @@ "id": "cba7e815", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.529915Z", - "iopub.status.busy": "2023-08-21T02:28:57.529812Z", - "iopub.status.idle": "2023-08-21T02:28:57.532108Z", - "shell.execute_reply": "2023-08-21T02:28:57.531849Z" + "iopub.execute_input": "2023-08-22T06:59:35.450840Z", + "iopub.status.busy": "2023-08-22T06:59:35.450725Z", + "iopub.status.idle": "2023-08-22T06:59:35.452944Z", + "shell.execute_reply": "2023-08-22T06:59:35.452674Z" } }, "outputs": [ @@ -1492,10 +1492,10 @@ "id": "97993185", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.533565Z", - "iopub.status.busy": "2023-08-21T02:28:57.533462Z", - "iopub.status.idle": "2023-08-21T02:28:57.537458Z", - "shell.execute_reply": "2023-08-21T02:28:57.537180Z" + "iopub.execute_input": "2023-08-22T06:59:35.454539Z", + "iopub.status.busy": "2023-08-22T06:59:35.454423Z", + "iopub.status.idle": "2023-08-22T06:59:35.458834Z", + "shell.execute_reply": "2023-08-22T06:59:35.458553Z" }, "lines_to_next_cell": 2 }, @@ -1544,10 +1544,10 @@ "id": "4bc774e9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.539054Z", - "iopub.status.busy": "2023-08-21T02:28:57.538952Z", - "iopub.status.idle": "2023-08-21T02:28:57.540857Z", - "shell.execute_reply": "2023-08-21T02:28:57.540537Z" + "iopub.execute_input": "2023-08-22T06:59:35.460658Z", + "iopub.status.busy": "2023-08-22T06:59:35.460534Z", + "iopub.status.idle": "2023-08-22T06:59:35.462216Z", + "shell.execute_reply": "2023-08-22T06:59:35.461957Z" } }, "outputs": [], @@ -1572,10 +1572,10 @@ "id": "8c6c0723", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.542568Z", - "iopub.status.busy": "2023-08-21T02:28:57.542481Z", - "iopub.status.idle": "2023-08-21T02:28:57.548927Z", - "shell.execute_reply": "2023-08-21T02:28:57.548581Z" + "iopub.execute_input": "2023-08-22T06:59:35.463858Z", + "iopub.status.busy": "2023-08-22T06:59:35.463759Z", + "iopub.status.idle": "2023-08-22T06:59:35.469987Z", + "shell.execute_reply": "2023-08-22T06:59:35.469710Z" }, "lines_to_next_cell": 0 }, @@ -1628,10 +1628,10 @@ "id": "cf8fd5ac", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.550601Z", - "iopub.status.busy": "2023-08-21T02:28:57.550496Z", - "iopub.status.idle": "2023-08-21T02:28:57.552737Z", - "shell.execute_reply": "2023-08-21T02:28:57.552471Z" + "iopub.execute_input": "2023-08-22T06:59:35.471584Z", + "iopub.status.busy": "2023-08-22T06:59:35.471470Z", + "iopub.status.idle": "2023-08-22T06:59:35.473834Z", + "shell.execute_reply": "2023-08-22T06:59:35.473552Z" }, "lines_to_next_cell": 2 }, @@ -1669,10 +1669,10 @@ "id": "bfd6b3f8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.554217Z", - "iopub.status.busy": "2023-08-21T02:28:57.554117Z", - "iopub.status.idle": "2023-08-21T02:28:57.556249Z", - "shell.execute_reply": "2023-08-21T02:28:57.555998Z" + "iopub.execute_input": "2023-08-22T06:59:35.475293Z", + "iopub.status.busy": "2023-08-22T06:59:35.475189Z", + "iopub.status.idle": "2023-08-22T06:59:35.477448Z", + "shell.execute_reply": "2023-08-22T06:59:35.477007Z" }, "lines_to_next_cell": 2 }, @@ -1707,10 +1707,10 @@ "id": "a1f75de4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.557716Z", - "iopub.status.busy": "2023-08-21T02:28:57.557623Z", - "iopub.status.idle": "2023-08-21T02:28:57.559886Z", - "shell.execute_reply": "2023-08-21T02:28:57.559582Z" + "iopub.execute_input": "2023-08-22T06:59:35.479354Z", + "iopub.status.busy": "2023-08-22T06:59:35.479219Z", + "iopub.status.idle": "2023-08-22T06:59:35.481609Z", + "shell.execute_reply": "2023-08-22T06:59:35.481305Z" }, "lines_to_next_cell": 2 }, @@ -1744,10 +1744,10 @@ "id": "82794178", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.561606Z", - "iopub.status.busy": "2023-08-21T02:28:57.561489Z", - "iopub.status.idle": "2023-08-21T02:28:57.563996Z", - "shell.execute_reply": "2023-08-21T02:28:57.563652Z" + "iopub.execute_input": "2023-08-22T06:59:35.483264Z", + "iopub.status.busy": "2023-08-22T06:59:35.483156Z", + "iopub.status.idle": "2023-08-22T06:59:35.485247Z", + "shell.execute_reply": "2023-08-22T06:59:35.484948Z" } }, "outputs": [ @@ -1782,10 +1782,10 @@ "id": "9b571047", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.565689Z", - "iopub.status.busy": "2023-08-21T02:28:57.565573Z", - "iopub.status.idle": "2023-08-21T02:28:57.568080Z", - "shell.execute_reply": "2023-08-21T02:28:57.567748Z" + "iopub.execute_input": "2023-08-22T06:59:35.486831Z", + "iopub.status.busy": "2023-08-22T06:59:35.486719Z", + "iopub.status.idle": "2023-08-22T06:59:35.489231Z", + "shell.execute_reply": "2023-08-22T06:59:35.488879Z" } }, "outputs": [], @@ -1809,10 +1809,10 @@ "id": "60f3d13a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.569793Z", - "iopub.status.busy": "2023-08-21T02:28:57.569674Z", - "iopub.status.idle": "2023-08-21T02:28:57.574322Z", - "shell.execute_reply": "2023-08-21T02:28:57.573986Z" + "iopub.execute_input": "2023-08-22T06:59:35.490888Z", + "iopub.status.busy": "2023-08-22T06:59:35.490781Z", + "iopub.status.idle": "2023-08-22T06:59:35.494972Z", + "shell.execute_reply": "2023-08-22T06:59:35.494699Z" }, "lines_to_next_cell": 2 }, @@ -1896,10 +1896,10 @@ "id": "96e680d9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.576103Z", - "iopub.status.busy": "2023-08-21T02:28:57.575991Z", - "iopub.status.idle": "2023-08-21T02:28:57.579068Z", - "shell.execute_reply": "2023-08-21T02:28:57.578805Z" + "iopub.execute_input": "2023-08-22T06:59:35.496550Z", + "iopub.status.busy": "2023-08-22T06:59:35.496460Z", + "iopub.status.idle": "2023-08-22T06:59:35.500050Z", + "shell.execute_reply": "2023-08-22T06:59:35.499652Z" }, "lines_to_next_cell": 2 }, @@ -1940,10 +1940,10 @@ "id": "b6695125", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.580746Z", - "iopub.status.busy": "2023-08-21T02:28:57.580639Z", - "iopub.status.idle": "2023-08-21T02:28:57.583327Z", - "shell.execute_reply": "2023-08-21T02:28:57.583073Z" + "iopub.execute_input": "2023-08-22T06:59:35.501821Z", + "iopub.status.busy": "2023-08-22T06:59:35.501696Z", + "iopub.status.idle": "2023-08-22T06:59:35.504118Z", + "shell.execute_reply": "2023-08-22T06:59:35.503779Z" }, "lines_to_next_cell": 2 }, @@ -1986,10 +1986,10 @@ "id": "3f38a14e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.584834Z", - "iopub.status.busy": "2023-08-21T02:28:57.584748Z", - "iopub.status.idle": "2023-08-21T02:28:57.587140Z", - "shell.execute_reply": "2023-08-21T02:28:57.586884Z" + "iopub.execute_input": "2023-08-22T06:59:35.505774Z", + "iopub.status.busy": "2023-08-22T06:59:35.505669Z", + "iopub.status.idle": "2023-08-22T06:59:35.508018Z", + "shell.execute_reply": "2023-08-22T06:59:35.507737Z" } }, "outputs": [ @@ -2051,10 +2051,10 @@ "id": "9b645803", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.588684Z", - "iopub.status.busy": "2023-08-21T02:28:57.588575Z", - "iopub.status.idle": "2023-08-21T02:28:57.592433Z", - "shell.execute_reply": "2023-08-21T02:28:57.592165Z" + "iopub.execute_input": "2023-08-22T06:59:35.510093Z", + "iopub.status.busy": "2023-08-22T06:59:35.509967Z", + "iopub.status.idle": "2023-08-22T06:59:35.514060Z", + "shell.execute_reply": "2023-08-22T06:59:35.513746Z" } }, "outputs": [ @@ -2091,10 +2091,10 @@ "id": "abfae544", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.593864Z", - "iopub.status.busy": "2023-08-21T02:28:57.593784Z", - "iopub.status.idle": "2023-08-21T02:28:57.596074Z", - "shell.execute_reply": "2023-08-21T02:28:57.595807Z" + "iopub.execute_input": "2023-08-22T06:59:35.515774Z", + "iopub.status.busy": "2023-08-22T06:59:35.515682Z", + "iopub.status.idle": "2023-08-22T06:59:35.518169Z", + "shell.execute_reply": "2023-08-22T06:59:35.517903Z" } }, "outputs": [ @@ -2130,10 +2130,10 @@ "id": "2a3bb41e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.597540Z", - "iopub.status.busy": "2023-08-21T02:28:57.597455Z", - "iopub.status.idle": "2023-08-21T02:28:57.599658Z", - "shell.execute_reply": "2023-08-21T02:28:57.599399Z" + "iopub.execute_input": "2023-08-22T06:59:35.519953Z", + "iopub.status.busy": "2023-08-22T06:59:35.519849Z", + "iopub.status.idle": "2023-08-22T06:59:35.522248Z", + "shell.execute_reply": "2023-08-22T06:59:35.521930Z" }, "lines_to_next_cell": 0 }, @@ -2172,10 +2172,10 @@ "id": "1c64310b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.601097Z", - "iopub.status.busy": "2023-08-21T02:28:57.601019Z", - "iopub.status.idle": "2023-08-21T02:28:57.605729Z", - "shell.execute_reply": "2023-08-21T02:28:57.605444Z" + "iopub.execute_input": "2023-08-22T06:59:35.523826Z", + "iopub.status.busy": "2023-08-22T06:59:35.523714Z", + "iopub.status.idle": "2023-08-22T06:59:35.528214Z", + "shell.execute_reply": "2023-08-22T06:59:35.527959Z" }, "lines_to_next_cell": 0 }, @@ -2257,10 +2257,10 @@ "id": "0c05c5a8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.607207Z", - "iopub.status.busy": "2023-08-21T02:28:57.607093Z", - "iopub.status.idle": "2023-08-21T02:28:57.609626Z", - "shell.execute_reply": "2023-08-21T02:28:57.609348Z" + "iopub.execute_input": "2023-08-22T06:59:35.530152Z", + "iopub.status.busy": "2023-08-22T06:59:35.530024Z", + "iopub.status.idle": "2023-08-22T06:59:35.532713Z", + "shell.execute_reply": "2023-08-22T06:59:35.532375Z" }, "lines_to_next_cell": 2 }, @@ -2313,10 +2313,10 @@ "id": "47d85305", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.611305Z", - "iopub.status.busy": "2023-08-21T02:28:57.611177Z", - "iopub.status.idle": "2023-08-21T02:28:57.615527Z", - "shell.execute_reply": "2023-08-21T02:28:57.615221Z" + "iopub.execute_input": "2023-08-22T06:59:35.534353Z", + "iopub.status.busy": "2023-08-22T06:59:35.534232Z", + "iopub.status.idle": "2023-08-22T06:59:35.537918Z", + "shell.execute_reply": "2023-08-22T06:59:35.537612Z" }, "lines_to_next_cell": 2 }, @@ -2354,10 +2354,10 @@ "id": "c553aadf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.617346Z", - "iopub.status.busy": "2023-08-21T02:28:57.617202Z", - "iopub.status.idle": "2023-08-21T02:28:57.619612Z", - "shell.execute_reply": "2023-08-21T02:28:57.619301Z" + "iopub.execute_input": "2023-08-22T06:59:35.539480Z", + "iopub.status.busy": "2023-08-22T06:59:35.539391Z", + "iopub.status.idle": "2023-08-22T06:59:35.541924Z", + "shell.execute_reply": "2023-08-22T06:59:35.541560Z" }, "lines_to_next_cell": 2 }, @@ -2391,10 +2391,10 @@ "id": "4604bd3d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.621294Z", - "iopub.status.busy": "2023-08-21T02:28:57.621168Z", - "iopub.status.idle": "2023-08-21T02:28:57.623355Z", - "shell.execute_reply": "2023-08-21T02:28:57.623086Z" + "iopub.execute_input": "2023-08-22T06:59:35.543604Z", + "iopub.status.busy": "2023-08-22T06:59:35.543514Z", + "iopub.status.idle": "2023-08-22T06:59:35.545770Z", + "shell.execute_reply": "2023-08-22T06:59:35.545483Z" }, "lines_to_next_cell": 2 }, @@ -2430,10 +2430,10 @@ "id": "5ac2cabe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.624902Z", - "iopub.status.busy": "2023-08-21T02:28:57.624775Z", - "iopub.status.idle": "2023-08-21T02:28:57.627201Z", - "shell.execute_reply": "2023-08-21T02:28:57.626911Z" + "iopub.execute_input": "2023-08-22T06:59:35.547333Z", + "iopub.status.busy": "2023-08-22T06:59:35.547227Z", + "iopub.status.idle": "2023-08-22T06:59:35.549381Z", + "shell.execute_reply": "2023-08-22T06:59:35.549124Z" } }, "outputs": [ @@ -2467,10 +2467,10 @@ "id": "f8623945", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.628781Z", - "iopub.status.busy": "2023-08-21T02:28:57.628673Z", - "iopub.status.idle": "2023-08-21T02:28:57.631185Z", - "shell.execute_reply": "2023-08-21T02:28:57.630909Z" + "iopub.execute_input": "2023-08-22T06:59:35.551288Z", + "iopub.status.busy": "2023-08-22T06:59:35.551134Z", + "iopub.status.idle": "2023-08-22T06:59:35.553370Z", + "shell.execute_reply": "2023-08-22T06:59:35.553102Z" }, "lines_to_next_cell": 0 }, @@ -2507,10 +2507,10 @@ "id": "0790f26e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.632749Z", - "iopub.status.busy": "2023-08-21T02:28:57.632622Z", - "iopub.status.idle": "2023-08-21T02:28:57.635950Z", - "shell.execute_reply": "2023-08-21T02:28:57.635645Z" + "iopub.execute_input": "2023-08-22T06:59:35.554980Z", + "iopub.status.busy": "2023-08-22T06:59:35.554846Z", + "iopub.status.idle": "2023-08-22T06:59:35.557806Z", + "shell.execute_reply": "2023-08-22T06:59:35.557573Z" }, "lines_to_next_cell": 2 }, @@ -2546,10 +2546,10 @@ "id": "4a8cf0ce", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.637431Z", - "iopub.status.busy": "2023-08-21T02:28:57.637345Z", - "iopub.status.idle": "2023-08-21T02:28:57.640679Z", - "shell.execute_reply": "2023-08-21T02:28:57.640387Z" + "iopub.execute_input": "2023-08-22T06:59:35.559235Z", + "iopub.status.busy": "2023-08-22T06:59:35.559122Z", + "iopub.status.idle": "2023-08-22T06:59:35.562452Z", + "shell.execute_reply": "2023-08-22T06:59:35.562071Z" }, "lines_to_next_cell": 0 }, @@ -2586,10 +2586,10 @@ "id": "94e7ff1a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.642306Z", - "iopub.status.busy": "2023-08-21T02:28:57.642190Z", - "iopub.status.idle": "2023-08-21T02:28:57.646804Z", - "shell.execute_reply": "2023-08-21T02:28:57.646534Z" + "iopub.execute_input": "2023-08-22T06:59:35.564237Z", + "iopub.status.busy": "2023-08-22T06:59:35.564138Z", + "iopub.status.idle": "2023-08-22T06:59:35.568635Z", + "shell.execute_reply": "2023-08-22T06:59:35.568359Z" } }, "outputs": [ @@ -2671,10 +2671,10 @@ "id": "137e23aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.648357Z", - "iopub.status.busy": "2023-08-21T02:28:57.648246Z", - "iopub.status.idle": "2023-08-21T02:28:57.651178Z", - "shell.execute_reply": "2023-08-21T02:28:57.650935Z" + "iopub.execute_input": "2023-08-22T06:59:35.570127Z", + "iopub.status.busy": "2023-08-22T06:59:35.570011Z", + "iopub.status.idle": "2023-08-22T06:59:35.573407Z", + "shell.execute_reply": "2023-08-22T06:59:35.573097Z" }, "lines_to_next_cell": 2 }, @@ -2721,10 +2721,10 @@ "id": "142c5217", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.652781Z", - "iopub.status.busy": "2023-08-21T02:28:57.652676Z", - "iopub.status.idle": "2023-08-21T02:28:57.665013Z", - "shell.execute_reply": "2023-08-21T02:28:57.664742Z" + "iopub.execute_input": "2023-08-22T06:59:35.575043Z", + "iopub.status.busy": "2023-08-22T06:59:35.574949Z", + "iopub.status.idle": "2023-08-22T06:59:35.585246Z", + "shell.execute_reply": "2023-08-22T06:59:35.584962Z" } }, "outputs": [ @@ -2809,10 +2809,10 @@ "id": "f5a272ee", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.666910Z", - "iopub.status.busy": "2023-08-21T02:28:57.666782Z", - "iopub.status.idle": "2023-08-21T02:28:57.669498Z", - "shell.execute_reply": "2023-08-21T02:28:57.669179Z" + "iopub.execute_input": "2023-08-22T06:59:35.587100Z", + "iopub.status.busy": "2023-08-22T06:59:35.586985Z", + "iopub.status.idle": "2023-08-22T06:59:35.589510Z", + "shell.execute_reply": "2023-08-22T06:59:35.589151Z" } }, "outputs": [ @@ -2846,10 +2846,10 @@ "id": "95e206a8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.671045Z", - "iopub.status.busy": "2023-08-21T02:28:57.670939Z", - "iopub.status.idle": "2023-08-21T02:28:57.679000Z", - "shell.execute_reply": "2023-08-21T02:28:57.678715Z" + "iopub.execute_input": "2023-08-22T06:59:35.591165Z", + "iopub.status.busy": "2023-08-22T06:59:35.591045Z", + "iopub.status.idle": "2023-08-22T06:59:35.598648Z", + "shell.execute_reply": "2023-08-22T06:59:35.598383Z" }, "lines_to_next_cell": 0 }, @@ -2901,10 +2901,10 @@ "id": "422563b7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.680643Z", - "iopub.status.busy": "2023-08-21T02:28:57.680560Z", - "iopub.status.idle": "2023-08-21T02:28:57.698929Z", - "shell.execute_reply": "2023-08-21T02:28:57.698652Z" + "iopub.execute_input": "2023-08-22T06:59:35.600269Z", + "iopub.status.busy": "2023-08-22T06:59:35.600186Z", + "iopub.status.idle": "2023-08-22T06:59:35.616715Z", + "shell.execute_reply": "2023-08-22T06:59:35.616375Z" }, "lines_to_next_cell": 2 }, @@ -2945,10 +2945,10 @@ "id": "583c860c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.700534Z", - "iopub.status.busy": "2023-08-21T02:28:57.700406Z", - "iopub.status.idle": "2023-08-21T02:28:57.702790Z", - "shell.execute_reply": "2023-08-21T02:28:57.702539Z" + "iopub.execute_input": "2023-08-22T06:59:35.618509Z", + "iopub.status.busy": "2023-08-22T06:59:35.618392Z", + "iopub.status.idle": "2023-08-22T06:59:35.620427Z", + "shell.execute_reply": "2023-08-22T06:59:35.620213Z" }, "lines_to_next_cell": 2 }, @@ -2982,10 +2982,10 @@ "id": "19ee3bf2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.704427Z", - "iopub.status.busy": "2023-08-21T02:28:57.704308Z", - "iopub.status.idle": "2023-08-21T02:28:57.707107Z", - "shell.execute_reply": "2023-08-21T02:28:57.706716Z" + "iopub.execute_input": "2023-08-22T06:59:35.622075Z", + "iopub.status.busy": "2023-08-22T06:59:35.621970Z", + "iopub.status.idle": "2023-08-22T06:59:35.624662Z", + "shell.execute_reply": "2023-08-22T06:59:35.624306Z" } }, "outputs": [], @@ -3031,10 +3031,10 @@ "id": "fdc0e5f1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.708836Z", - "iopub.status.busy": "2023-08-21T02:28:57.708707Z", - "iopub.status.idle": "2023-08-21T02:28:57.710507Z", - "shell.execute_reply": "2023-08-21T02:28:57.710220Z" + "iopub.execute_input": "2023-08-22T06:59:35.626535Z", + "iopub.status.busy": "2023-08-22T06:59:35.626411Z", + "iopub.status.idle": "2023-08-22T06:59:35.628326Z", + "shell.execute_reply": "2023-08-22T06:59:35.628023Z" }, "lines_to_next_cell": 0 }, @@ -3070,10 +3070,10 @@ "id": "2bb9d48b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.712025Z", - "iopub.status.busy": "2023-08-21T02:28:57.711919Z", - "iopub.status.idle": "2023-08-21T02:28:57.717961Z", - "shell.execute_reply": "2023-08-21T02:28:57.717623Z" + "iopub.execute_input": "2023-08-22T06:59:35.630001Z", + "iopub.status.busy": "2023-08-22T06:59:35.629870Z", + "iopub.status.idle": "2023-08-22T06:59:35.636000Z", + "shell.execute_reply": "2023-08-22T06:59:35.635697Z" }, "lines_to_next_cell": 0 }, @@ -3098,10 +3098,10 @@ "id": "649b57b3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.719733Z", - "iopub.status.busy": "2023-08-21T02:28:57.719643Z", - "iopub.status.idle": "2023-08-21T02:28:57.725788Z", - "shell.execute_reply": "2023-08-21T02:28:57.725511Z" + "iopub.execute_input": "2023-08-22T06:59:35.637746Z", + "iopub.status.busy": "2023-08-22T06:59:35.637618Z", + "iopub.status.idle": "2023-08-22T06:59:35.643400Z", + "shell.execute_reply": "2023-08-22T06:59:35.643102Z" } }, "outputs": [ @@ -3154,10 +3154,10 @@ "id": "d0aafd5e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.727208Z", - "iopub.status.busy": "2023-08-21T02:28:57.727114Z", - "iopub.status.idle": "2023-08-21T02:28:57.730769Z", - "shell.execute_reply": "2023-08-21T02:28:57.730507Z" + "iopub.execute_input": "2023-08-22T06:59:35.644912Z", + "iopub.status.busy": "2023-08-22T06:59:35.644814Z", + "iopub.status.idle": "2023-08-22T06:59:35.648144Z", + "shell.execute_reply": "2023-08-22T06:59:35.647828Z" }, "lines_to_next_cell": 0 }, @@ -3189,10 +3189,10 @@ "id": "ad02fb42", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:57.732302Z", - "iopub.status.busy": "2023-08-21T02:28:57.732202Z", - "iopub.status.idle": "2023-08-21T02:28:58.059169Z", - "shell.execute_reply": "2023-08-21T02:28:58.058739Z" + "iopub.execute_input": "2023-08-22T06:59:35.649991Z", + "iopub.status.busy": "2023-08-22T06:59:35.649857Z", + "iopub.status.idle": "2023-08-22T06:59:35.851869Z", + "shell.execute_reply": "2023-08-22T06:59:35.850800Z" }, "lines_to_next_cell": 2 }, @@ -3242,10 +3242,10 @@ "id": "901f772e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.061293Z", - "iopub.status.busy": "2023-08-21T02:28:58.061165Z", - "iopub.status.idle": "2023-08-21T02:28:58.067519Z", - "shell.execute_reply": "2023-08-21T02:28:58.067152Z" + "iopub.execute_input": "2023-08-22T06:59:35.857360Z", + "iopub.status.busy": "2023-08-22T06:59:35.856994Z", + "iopub.status.idle": "2023-08-22T06:59:35.868149Z", + "shell.execute_reply": "2023-08-22T06:59:35.867281Z" } }, "outputs": [ @@ -3327,10 +3327,10 @@ "id": "684f8941", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.069340Z", - "iopub.status.busy": "2023-08-21T02:28:58.069223Z", - "iopub.status.idle": "2023-08-21T02:28:58.071737Z", - "shell.execute_reply": "2023-08-21T02:28:58.071247Z" + "iopub.execute_input": "2023-08-22T06:59:35.872302Z", + "iopub.status.busy": "2023-08-22T06:59:35.871992Z", + "iopub.status.idle": "2023-08-22T06:59:35.881501Z", + "shell.execute_reply": "2023-08-22T06:59:35.879490Z" }, "lines_to_next_cell": 2 }, @@ -3371,10 +3371,10 @@ "id": "4d984cf0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.073571Z", - "iopub.status.busy": "2023-08-21T02:28:58.073457Z", - "iopub.status.idle": "2023-08-21T02:28:58.146516Z", - "shell.execute_reply": "2023-08-21T02:28:58.146020Z" + "iopub.execute_input": "2023-08-22T06:59:35.885276Z", + "iopub.status.busy": "2023-08-22T06:59:35.884561Z", + "iopub.status.idle": "2023-08-22T06:59:36.032887Z", + "shell.execute_reply": "2023-08-22T06:59:36.032527Z" }, "lines_to_next_cell": 0 }, @@ -3438,10 +3438,10 @@ "id": "d24f4e50", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.148624Z", - "iopub.status.busy": "2023-08-21T02:28:58.148360Z", - "iopub.status.idle": "2023-08-21T02:28:58.773984Z", - "shell.execute_reply": "2023-08-21T02:28:58.773325Z" + "iopub.execute_input": "2023-08-22T06:59:36.035053Z", + "iopub.status.busy": "2023-08-22T06:59:36.034905Z", + "iopub.status.idle": "2023-08-22T06:59:36.559516Z", + "shell.execute_reply": "2023-08-22T06:59:36.557112Z" } }, "outputs": [ @@ -3535,10 +3535,10 @@ "id": "25152580", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.778052Z", - "iopub.status.busy": "2023-08-21T02:28:58.777754Z", - "iopub.status.idle": "2023-08-21T02:28:58.791505Z", - "shell.execute_reply": "2023-08-21T02:28:58.790362Z" + "iopub.execute_input": "2023-08-22T06:59:36.564551Z", + "iopub.status.busy": "2023-08-22T06:59:36.564196Z", + "iopub.status.idle": "2023-08-22T06:59:36.582460Z", + "shell.execute_reply": "2023-08-22T06:59:36.580635Z" } }, "outputs": [ @@ -3610,10 +3610,10 @@ "id": "b8ea6e08", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.798441Z", - "iopub.status.busy": "2023-08-21T02:28:58.798187Z", - "iopub.status.idle": "2023-08-21T02:28:58.805015Z", - "shell.execute_reply": "2023-08-21T02:28:58.804266Z" + "iopub.execute_input": "2023-08-22T06:59:36.590532Z", + "iopub.status.busy": "2023-08-22T06:59:36.590038Z", + "iopub.status.idle": "2023-08-22T06:59:36.605488Z", + "shell.execute_reply": "2023-08-22T06:59:36.602880Z" }, "lines_to_next_cell": 0 }, @@ -3650,10 +3650,10 @@ "id": "def80d79", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.808423Z", - "iopub.status.busy": "2023-08-21T02:28:58.807929Z", - "iopub.status.idle": "2023-08-21T02:28:58.826147Z", - "shell.execute_reply": "2023-08-21T02:28:58.825308Z" + "iopub.execute_input": "2023-08-22T06:59:36.610362Z", + "iopub.status.busy": "2023-08-22T06:59:36.609709Z", + "iopub.status.idle": "2023-08-22T06:59:36.624089Z", + "shell.execute_reply": "2023-08-22T06:59:36.623200Z" }, "lines_to_next_cell": 0 }, @@ -3676,10 +3676,10 @@ "id": "f899d5ab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.838005Z", - "iopub.status.busy": "2023-08-21T02:28:58.837542Z", - "iopub.status.idle": "2023-08-21T02:28:58.842697Z", - "shell.execute_reply": "2023-08-21T02:28:58.841989Z" + "iopub.execute_input": "2023-08-22T06:59:36.630386Z", + "iopub.status.busy": "2023-08-22T06:59:36.629966Z", + "iopub.status.idle": "2023-08-22T06:59:36.636403Z", + "shell.execute_reply": "2023-08-22T06:59:36.635734Z" } }, "outputs": [ @@ -3718,10 +3718,10 @@ "id": "76f4cea5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.846154Z", - "iopub.status.busy": "2023-08-21T02:28:58.845860Z", - "iopub.status.idle": "2023-08-21T02:28:58.936368Z", - "shell.execute_reply": "2023-08-21T02:28:58.931496Z" + "iopub.execute_input": "2023-08-22T06:59:36.642541Z", + "iopub.status.busy": "2023-08-22T06:59:36.642195Z", + "iopub.status.idle": "2023-08-22T06:59:36.742213Z", + "shell.execute_reply": "2023-08-22T06:59:36.740543Z" } }, "outputs": [ @@ -4122,10 +4122,10 @@ "id": "5778ada8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.941746Z", - "iopub.status.busy": "2023-08-21T02:28:58.941366Z", - "iopub.status.idle": "2023-08-21T02:28:58.948890Z", - "shell.execute_reply": "2023-08-21T02:28:58.947630Z" + "iopub.execute_input": "2023-08-22T06:59:36.748318Z", + "iopub.status.busy": "2023-08-22T06:59:36.747980Z", + "iopub.status.idle": "2023-08-22T06:59:36.753044Z", + "shell.execute_reply": "2023-08-22T06:59:36.752001Z" }, "lines_to_next_cell": 0 }, @@ -4149,10 +4149,10 @@ "id": "c6da14b9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:58.953721Z", - "iopub.status.busy": "2023-08-21T02:28:58.953330Z", - "iopub.status.idle": "2023-08-21T02:28:59.054283Z", - "shell.execute_reply": "2023-08-21T02:28:59.053564Z" + "iopub.execute_input": "2023-08-22T06:59:36.761294Z", + "iopub.status.busy": "2023-08-22T06:59:36.760741Z", + "iopub.status.idle": "2023-08-22T06:59:36.824675Z", + "shell.execute_reply": "2023-08-22T06:59:36.823419Z" } }, "outputs": [ @@ -4559,10 +4559,10 @@ "id": "461d57c5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.063565Z", - "iopub.status.busy": "2023-08-21T02:28:59.063233Z", - "iopub.status.idle": "2023-08-21T02:28:59.071787Z", - "shell.execute_reply": "2023-08-21T02:28:59.070620Z" + "iopub.execute_input": "2023-08-22T06:59:36.832613Z", + "iopub.status.busy": "2023-08-22T06:59:36.831243Z", + "iopub.status.idle": "2023-08-22T06:59:36.839557Z", + "shell.execute_reply": "2023-08-22T06:59:36.838190Z" } }, "outputs": [ @@ -4596,10 +4596,10 @@ "id": "05d33247", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.077767Z", - "iopub.status.busy": "2023-08-21T02:28:59.077090Z", - "iopub.status.idle": "2023-08-21T02:28:59.085265Z", - "shell.execute_reply": "2023-08-21T02:28:59.084291Z" + "iopub.execute_input": "2023-08-22T06:59:36.846635Z", + "iopub.status.busy": "2023-08-22T06:59:36.846336Z", + "iopub.status.idle": "2023-08-22T06:59:36.854114Z", + "shell.execute_reply": "2023-08-22T06:59:36.853108Z" }, "lines_to_next_cell": 2 }, @@ -4639,10 +4639,10 @@ "id": "bee42b38", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.089190Z", - "iopub.status.busy": "2023-08-21T02:28:59.088696Z", - "iopub.status.idle": "2023-08-21T02:28:59.099445Z", - "shell.execute_reply": "2023-08-21T02:28:59.098750Z" + "iopub.execute_input": "2023-08-22T06:59:36.861230Z", + "iopub.status.busy": "2023-08-22T06:59:36.860269Z", + "iopub.status.idle": "2023-08-22T06:59:36.869236Z", + "shell.execute_reply": "2023-08-22T06:59:36.868250Z" }, "lines_to_next_cell": 0 }, @@ -4688,10 +4688,10 @@ "id": "4aa60857", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.103423Z", - "iopub.status.busy": "2023-08-21T02:28:59.103057Z", - "iopub.status.idle": "2023-08-21T02:28:59.112346Z", - "shell.execute_reply": "2023-08-21T02:28:59.111002Z" + "iopub.execute_input": "2023-08-22T06:59:36.876998Z", + "iopub.status.busy": "2023-08-22T06:59:36.876016Z", + "iopub.status.idle": "2023-08-22T06:59:36.883621Z", + "shell.execute_reply": "2023-08-22T06:59:36.882372Z" }, "lines_to_next_cell": 0 }, @@ -4745,10 +4745,10 @@ "id": "894d3e2c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.116495Z", - "iopub.status.busy": "2023-08-21T02:28:59.116193Z", - "iopub.status.idle": "2023-08-21T02:28:59.250351Z", - "shell.execute_reply": "2023-08-21T02:28:59.249608Z" + "iopub.execute_input": "2023-08-22T06:59:36.889441Z", + "iopub.status.busy": "2023-08-22T06:59:36.888780Z", + "iopub.status.idle": "2023-08-22T06:59:37.004601Z", + "shell.execute_reply": "2023-08-22T06:59:37.003102Z" } }, "outputs": [ @@ -4787,10 +4787,10 @@ "id": "d636746e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.253708Z", - "iopub.status.busy": "2023-08-21T02:28:59.253326Z", - "iopub.status.idle": "2023-08-21T02:28:59.260644Z", - "shell.execute_reply": "2023-08-21T02:28:59.259501Z" + "iopub.execute_input": "2023-08-22T06:59:37.014352Z", + "iopub.status.busy": "2023-08-22T06:59:37.013713Z", + "iopub.status.idle": "2023-08-22T06:59:37.022133Z", + "shell.execute_reply": "2023-08-22T06:59:37.021189Z" } }, "outputs": [], @@ -4816,10 +4816,10 @@ "id": "ce6a1623", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.264364Z", - "iopub.status.busy": "2023-08-21T02:28:59.264112Z", - "iopub.status.idle": "2023-08-21T02:28:59.384102Z", - "shell.execute_reply": "2023-08-21T02:28:59.383714Z" + "iopub.execute_input": "2023-08-22T06:59:37.030881Z", + "iopub.status.busy": "2023-08-22T06:59:37.030334Z", + "iopub.status.idle": "2023-08-22T06:59:37.126992Z", + "shell.execute_reply": "2023-08-22T06:59:37.126566Z" } }, "outputs": [ @@ -4862,10 +4862,10 @@ "id": "9fb8b759", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.386009Z", - "iopub.status.busy": "2023-08-21T02:28:59.385867Z", - "iopub.status.idle": "2023-08-21T02:28:59.478822Z", - "shell.execute_reply": "2023-08-21T02:28:59.477841Z" + "iopub.execute_input": "2023-08-22T06:59:37.128893Z", + "iopub.status.busy": "2023-08-22T06:59:37.128737Z", + "iopub.status.idle": "2023-08-22T06:59:37.220758Z", + "shell.execute_reply": "2023-08-22T06:59:37.219419Z" } }, "outputs": [], @@ -4887,10 +4887,10 @@ "id": "ee272341", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.482988Z", - "iopub.status.busy": "2023-08-21T02:28:59.482517Z", - "iopub.status.idle": "2023-08-21T02:28:59.506926Z", - "shell.execute_reply": "2023-08-21T02:28:59.506267Z" + "iopub.execute_input": "2023-08-22T06:59:37.227931Z", + "iopub.status.busy": "2023-08-22T06:59:37.227304Z", + "iopub.status.idle": "2023-08-22T06:59:37.252847Z", + "shell.execute_reply": "2023-08-22T06:59:37.251756Z" }, "lines_to_next_cell": 0 }, @@ -4921,10 +4921,10 @@ "id": "1f5bde07", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.510834Z", - "iopub.status.busy": "2023-08-21T02:28:59.510470Z", - "iopub.status.idle": "2023-08-21T02:28:59.792175Z", - "shell.execute_reply": "2023-08-21T02:28:59.791845Z" + "iopub.execute_input": "2023-08-22T06:59:37.263402Z", + "iopub.status.busy": "2023-08-22T06:59:37.262273Z", + "iopub.status.idle": "2023-08-22T06:59:37.482693Z", + "shell.execute_reply": "2023-08-22T06:59:37.479472Z" }, "lines_to_next_cell": 0 }, @@ -4933,7 +4933,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_80940/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_84444/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n" ] }, @@ -4978,10 +4978,10 @@ "id": "b0bd66a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:28:59.793823Z", - "iopub.status.busy": "2023-08-21T02:28:59.793732Z", - "iopub.status.idle": "2023-08-21T02:28:59.904438Z", - "shell.execute_reply": "2023-08-21T02:28:59.904151Z" + "iopub.execute_input": "2023-08-22T06:59:37.487973Z", + "iopub.status.busy": "2023-08-22T06:59:37.486725Z", + "iopub.status.idle": "2023-08-22T06:59:37.588898Z", + "shell.execute_reply": "2023-08-22T06:59:37.588557Z" } }, "outputs": [ @@ -5031,7 +5031,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -5044,7 +5044,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch05-resample-lab.Rmd b/Ch05-resample-lab.Rmd index 34e2d1d..1733c87 100644 --- a/Ch05-resample-lab.Rmd +++ b/Ch05-resample-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch05-resample-lab.ipynb b/Ch05-resample-lab.ipynb index 85c1f65..2314c64 100644 --- a/Ch05-resample-lab.ipynb +++ b/Ch05-resample-lab.ipynb @@ -29,10 +29,10 @@ "id": "e7712cfe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:01.252458Z", - "iopub.status.busy": "2023-08-21T02:29:01.251970Z", - "iopub.status.idle": "2023-08-21T02:29:02.044045Z", - "shell.execute_reply": "2023-08-21T02:29:02.043730Z" + "iopub.execute_input": "2023-08-22T06:59:40.827828Z", + "iopub.status.busy": "2023-08-22T06:59:40.827725Z", + "iopub.status.idle": "2023-08-22T06:59:41.658013Z", + "shell.execute_reply": "2023-08-22T06:59:41.657645Z" }, "lines_to_next_cell": 2 }, @@ -61,10 +61,10 @@ "id": "21c2ed4f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.045927Z", - "iopub.status.busy": "2023-08-21T02:29:02.045761Z", - "iopub.status.idle": "2023-08-21T02:29:02.047761Z", - "shell.execute_reply": "2023-08-21T02:29:02.047491Z" + "iopub.execute_input": "2023-08-22T06:59:41.659951Z", + "iopub.status.busy": "2023-08-22T06:59:41.659798Z", + "iopub.status.idle": "2023-08-22T06:59:41.661744Z", + "shell.execute_reply": "2023-08-22T06:59:41.661437Z" }, "lines_to_next_cell": 2 }, @@ -105,10 +105,10 @@ "id": "8af59641", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.049239Z", - "iopub.status.busy": "2023-08-21T02:29:02.049145Z", - "iopub.status.idle": "2023-08-21T02:29:02.055524Z", - "shell.execute_reply": "2023-08-21T02:29:02.055162Z" + "iopub.execute_input": "2023-08-22T06:59:41.663216Z", + "iopub.status.busy": "2023-08-22T06:59:41.663117Z", + "iopub.status.idle": "2023-08-22T06:59:41.667680Z", + "shell.execute_reply": "2023-08-22T06:59:41.667343Z" } }, "outputs": [], @@ -133,10 +133,10 @@ "id": "d9b0b7c8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.057278Z", - "iopub.status.busy": "2023-08-21T02:29:02.057182Z", - "iopub.status.idle": "2023-08-21T02:29:02.062537Z", - "shell.execute_reply": "2023-08-21T02:29:02.062265Z" + "iopub.execute_input": "2023-08-22T06:59:41.669501Z", + "iopub.status.busy": "2023-08-22T06:59:41.669395Z", + "iopub.status.idle": "2023-08-22T06:59:41.674461Z", + "shell.execute_reply": "2023-08-22T06:59:41.674176Z" } }, "outputs": [], @@ -163,10 +163,10 @@ "id": "3e77d831", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.064056Z", - "iopub.status.busy": "2023-08-21T02:29:02.063966Z", - "iopub.status.idle": "2023-08-21T02:29:02.068279Z", - "shell.execute_reply": "2023-08-21T02:29:02.068024Z" + "iopub.execute_input": "2023-08-22T06:59:41.676257Z", + "iopub.status.busy": "2023-08-22T06:59:41.676134Z", + "iopub.status.idle": "2023-08-22T06:59:41.680728Z", + "shell.execute_reply": "2023-08-22T06:59:41.680456Z" } }, "outputs": [ @@ -207,10 +207,10 @@ "id": "0aa4bfcc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.069789Z", - "iopub.status.busy": "2023-08-21T02:29:02.069682Z", - "iopub.status.idle": "2023-08-21T02:29:02.071953Z", - "shell.execute_reply": "2023-08-21T02:29:02.071703Z" + "iopub.execute_input": "2023-08-22T06:59:41.682418Z", + "iopub.status.busy": "2023-08-22T06:59:41.682304Z", + "iopub.status.idle": "2023-08-22T06:59:41.684531Z", + "shell.execute_reply": "2023-08-22T06:59:41.684276Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "id": "a0dbd55f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.073322Z", - "iopub.status.busy": "2023-08-21T02:29:02.073229Z", - "iopub.status.idle": "2023-08-21T02:29:02.088464Z", - "shell.execute_reply": "2023-08-21T02:29:02.088192Z" + "iopub.execute_input": "2023-08-22T06:59:41.686084Z", + "iopub.status.busy": "2023-08-22T06:59:41.685977Z", + "iopub.status.idle": "2023-08-22T06:59:41.701551Z", + "shell.execute_reply": "2023-08-22T06:59:41.701265Z" } }, "outputs": [ @@ -294,10 +294,10 @@ "id": "885136a4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.089889Z", - "iopub.status.busy": "2023-08-21T02:29:02.089804Z", - "iopub.status.idle": "2023-08-21T02:29:02.105353Z", - "shell.execute_reply": "2023-08-21T02:29:02.105089Z" + "iopub.execute_input": "2023-08-22T06:59:41.703198Z", + "iopub.status.busy": "2023-08-22T06:59:41.703080Z", + "iopub.status.idle": "2023-08-22T06:59:41.719142Z", + "shell.execute_reply": "2023-08-22T06:59:41.718778Z" } }, "outputs": [ @@ -377,10 +377,10 @@ "id": "6d957d8c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:02.106979Z", - "iopub.status.busy": "2023-08-21T02:29:02.106884Z", - "iopub.status.idle": "2023-08-21T02:29:03.184550Z", - "shell.execute_reply": "2023-08-21T02:29:03.184259Z" + "iopub.execute_input": "2023-08-22T06:59:41.721067Z", + "iopub.status.busy": "2023-08-22T06:59:41.720935Z", + "iopub.status.idle": "2023-08-22T06:59:42.841536Z", + "shell.execute_reply": "2023-08-22T06:59:42.841242Z" }, "lines_to_next_cell": 0 }, @@ -445,10 +445,10 @@ "id": "e2b5ce95", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.186226Z", - "iopub.status.busy": "2023-08-21T02:29:03.186108Z", - "iopub.status.idle": "2023-08-21T02:29:03.782413Z", - "shell.execute_reply": "2023-08-21T02:29:03.782122Z" + "iopub.execute_input": "2023-08-22T06:59:42.843438Z", + "iopub.status.busy": "2023-08-22T06:59:42.843308Z", + "iopub.status.idle": "2023-08-22T06:59:43.408832Z", + "shell.execute_reply": "2023-08-22T06:59:43.408347Z" }, "lines_to_next_cell": 0 }, @@ -502,10 +502,10 @@ "id": "1dda1bd7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.783997Z", - "iopub.status.busy": "2023-08-21T02:29:03.783886Z", - "iopub.status.idle": "2023-08-21T02:29:03.786132Z", - "shell.execute_reply": "2023-08-21T02:29:03.785881Z" + "iopub.execute_input": "2023-08-22T06:59:43.411083Z", + "iopub.status.busy": "2023-08-22T06:59:43.410940Z", + "iopub.status.idle": "2023-08-22T06:59:43.413889Z", + "shell.execute_reply": "2023-08-22T06:59:43.413456Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "fb25fa70", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.787622Z", - "iopub.status.busy": "2023-08-21T02:29:03.787525Z", - "iopub.status.idle": "2023-08-21T02:29:03.809671Z", - "shell.execute_reply": "2023-08-21T02:29:03.809398Z" + "iopub.execute_input": "2023-08-22T06:59:43.415865Z", + "iopub.status.busy": "2023-08-22T06:59:43.415724Z", + "iopub.status.idle": "2023-08-22T06:59:43.437716Z", + "shell.execute_reply": "2023-08-22T06:59:43.437344Z" }, "lines_to_next_cell": 0 }, @@ -609,10 +609,10 @@ "id": "d78795cd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.811123Z", - "iopub.status.busy": "2023-08-21T02:29:03.811046Z", - "iopub.status.idle": "2023-08-21T02:29:03.817840Z", - "shell.execute_reply": "2023-08-21T02:29:03.817582Z" + "iopub.execute_input": "2023-08-22T06:59:43.439940Z", + "iopub.status.busy": "2023-08-22T06:59:43.439783Z", + "iopub.status.idle": "2023-08-22T06:59:43.447580Z", + "shell.execute_reply": "2023-08-22T06:59:43.447286Z" }, "lines_to_next_cell": 2 }, @@ -653,10 +653,10 @@ "id": "0407ad56", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.819308Z", - "iopub.status.busy": "2023-08-21T02:29:03.819228Z", - "iopub.status.idle": "2023-08-21T02:29:03.851921Z", - "shell.execute_reply": "2023-08-21T02:29:03.851658Z" + "iopub.execute_input": "2023-08-22T06:59:43.449591Z", + "iopub.status.busy": "2023-08-22T06:59:43.449452Z", + "iopub.status.idle": "2023-08-22T06:59:43.483638Z", + "shell.execute_reply": "2023-08-22T06:59:43.483222Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "f04f15bd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.853415Z", - "iopub.status.busy": "2023-08-21T02:29:03.853334Z", - "iopub.status.idle": "2023-08-21T02:29:03.857370Z", - "shell.execute_reply": "2023-08-21T02:29:03.857115Z" + "iopub.execute_input": "2023-08-22T06:59:43.485600Z", + "iopub.status.busy": "2023-08-22T06:59:43.485469Z", + "iopub.status.idle": "2023-08-22T06:59:43.488993Z", + "shell.execute_reply": "2023-08-22T06:59:43.488698Z" }, "lines_to_next_cell": 0 }, @@ -761,10 +761,10 @@ "id": "f98c0323", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.858828Z", - "iopub.status.busy": "2023-08-21T02:29:03.858753Z", - "iopub.status.idle": "2023-08-21T02:29:03.861443Z", - "shell.execute_reply": "2023-08-21T02:29:03.861198Z" + "iopub.execute_input": "2023-08-22T06:59:43.490952Z", + "iopub.status.busy": "2023-08-22T06:59:43.490857Z", + "iopub.status.idle": "2023-08-22T06:59:43.494027Z", + "shell.execute_reply": "2023-08-22T06:59:43.493695Z" } }, "outputs": [ @@ -800,10 +800,10 @@ "id": "bcd40175", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.862933Z", - "iopub.status.busy": "2023-08-21T02:29:03.862830Z", - "iopub.status.idle": "2023-08-21T02:29:03.865766Z", - "shell.execute_reply": "2023-08-21T02:29:03.865514Z" + "iopub.execute_input": "2023-08-22T06:59:43.495852Z", + "iopub.status.busy": "2023-08-22T06:59:43.495690Z", + "iopub.status.idle": "2023-08-22T06:59:43.498806Z", + "shell.execute_reply": "2023-08-22T06:59:43.498500Z" }, "lines_to_next_cell": 2 }, @@ -843,10 +843,10 @@ "id": "ab6602cd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.867170Z", - "iopub.status.busy": "2023-08-21T02:29:03.867072Z", - "iopub.status.idle": "2023-08-21T02:29:03.869326Z", - "shell.execute_reply": "2023-08-21T02:29:03.869094Z" + "iopub.execute_input": "2023-08-22T06:59:43.500527Z", + "iopub.status.busy": "2023-08-22T06:59:43.500424Z", + "iopub.status.idle": "2023-08-22T06:59:43.502886Z", + "shell.execute_reply": "2023-08-22T06:59:43.502621Z" }, "lines_to_next_cell": 0 }, @@ -888,10 +888,10 @@ "id": "4a323513", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:03.870755Z", - "iopub.status.busy": "2023-08-21T02:29:03.870664Z", - "iopub.status.idle": "2023-08-21T02:29:04.157907Z", - "shell.execute_reply": "2023-08-21T02:29:04.157623Z" + "iopub.execute_input": "2023-08-22T06:59:43.504576Z", + "iopub.status.busy": "2023-08-22T06:59:43.504471Z", + "iopub.status.idle": "2023-08-22T06:59:43.776972Z", + "shell.execute_reply": "2023-08-22T06:59:43.776638Z" } }, "outputs": [ @@ -954,10 +954,10 @@ "id": "0220f3af", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:04.159500Z", - "iopub.status.busy": "2023-08-21T02:29:04.159419Z", - "iopub.status.idle": "2023-08-21T02:29:04.161332Z", - "shell.execute_reply": "2023-08-21T02:29:04.161073Z" + "iopub.execute_input": "2023-08-22T06:59:43.778737Z", + "iopub.status.busy": "2023-08-22T06:59:43.778622Z", + "iopub.status.idle": "2023-08-22T06:59:43.780660Z", + "shell.execute_reply": "2023-08-22T06:59:43.780373Z" }, "lines_to_next_cell": 0 }, @@ -989,10 +989,10 @@ "id": "62037dcb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:04.162950Z", - "iopub.status.busy": "2023-08-21T02:29:04.162849Z", - "iopub.status.idle": "2023-08-21T02:29:04.164486Z", - "shell.execute_reply": "2023-08-21T02:29:04.164241Z" + "iopub.execute_input": "2023-08-22T06:59:43.782304Z", + "iopub.status.busy": "2023-08-22T06:59:43.782178Z", + "iopub.status.idle": "2023-08-22T06:59:43.783926Z", + "shell.execute_reply": "2023-08-22T06:59:43.783681Z" }, "lines_to_next_cell": 0 }, @@ -1022,10 +1022,10 @@ "id": "b8bdb7a4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:04.165879Z", - "iopub.status.busy": "2023-08-21T02:29:04.165798Z", - "iopub.status.idle": "2023-08-21T02:29:04.194029Z", - "shell.execute_reply": "2023-08-21T02:29:04.193764Z" + "iopub.execute_input": "2023-08-22T06:59:43.785584Z", + "iopub.status.busy": "2023-08-22T06:59:43.785479Z", + "iopub.status.idle": "2023-08-22T06:59:43.814180Z", + "shell.execute_reply": "2023-08-22T06:59:43.813871Z" }, "lines_to_next_cell": 0 }, @@ -1073,10 +1073,10 @@ "id": "36808258", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:04.195612Z", - "iopub.status.busy": "2023-08-21T02:29:04.195529Z", - "iopub.status.idle": "2023-08-21T02:29:06.747175Z", - "shell.execute_reply": "2023-08-21T02:29:06.746638Z" + "iopub.execute_input": "2023-08-22T06:59:43.815884Z", + "iopub.status.busy": "2023-08-22T06:59:43.815750Z", + "iopub.status.idle": "2023-08-22T06:59:46.317022Z", + "shell.execute_reply": "2023-08-22T06:59:46.316748Z" }, "lines_to_next_cell": 2 }, @@ -1123,10 +1123,10 @@ "id": "c9aea297", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:06.749614Z", - "iopub.status.busy": "2023-08-21T02:29:06.749433Z", - "iopub.status.idle": "2023-08-21T02:29:06.812583Z", - "shell.execute_reply": "2023-08-21T02:29:06.812298Z" + "iopub.execute_input": "2023-08-22T06:59:46.318852Z", + "iopub.status.busy": "2023-08-22T06:59:46.318744Z", + "iopub.status.idle": "2023-08-22T06:59:46.401268Z", + "shell.execute_reply": "2023-08-22T06:59:46.400928Z" }, "lines_to_next_cell": 2 }, @@ -1195,10 +1195,10 @@ "id": "79c56529", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:06.814267Z", - "iopub.status.busy": "2023-08-21T02:29:06.814125Z", - "iopub.status.idle": "2023-08-21T02:29:10.162177Z", - "shell.execute_reply": "2023-08-21T02:29:10.161855Z" + "iopub.execute_input": "2023-08-22T06:59:46.403009Z", + "iopub.status.busy": "2023-08-22T06:59:46.402852Z", + "iopub.status.idle": "2023-08-22T06:59:50.039894Z", + "shell.execute_reply": "2023-08-22T06:59:50.039566Z" } }, "outputs": [ @@ -1238,10 +1238,10 @@ "id": "4d0b4edc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:10.163852Z", - "iopub.status.busy": "2023-08-21T02:29:10.163742Z", - "iopub.status.idle": "2023-08-21T02:29:10.173834Z", - "shell.execute_reply": "2023-08-21T02:29:10.173578Z" + "iopub.execute_input": "2023-08-22T06:59:50.041629Z", + "iopub.status.busy": "2023-08-22T06:59:50.041514Z", + "iopub.status.idle": "2023-08-22T06:59:50.052046Z", + "shell.execute_reply": "2023-08-22T06:59:50.051690Z" }, "lines_to_next_cell": 0 }, @@ -1279,7 +1279,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -1292,7 +1292,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch06-varselect-lab.Rmd b/Ch06-varselect-lab.Rmd index 9396072..4979e46 100644 --- a/Ch06-varselect-lab.Rmd +++ b/Ch06-varselect-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch06-varselect-lab.ipynb b/Ch06-varselect-lab.ipynb index f3342ea..94ddd1b 100644 --- a/Ch06-varselect-lab.ipynb +++ b/Ch06-varselect-lab.ipynb @@ -26,10 +26,10 @@ "id": "638bdae9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:11.258581Z", - "iopub.status.busy": "2023-08-21T02:29:11.258299Z", - "iopub.status.idle": "2023-08-21T02:29:12.282137Z", - "shell.execute_reply": "2023-08-21T02:29:12.281824Z" + "iopub.execute_input": "2023-08-22T06:59:53.093245Z", + "iopub.status.busy": "2023-08-22T06:59:53.093143Z", + "iopub.status.idle": "2023-08-22T06:59:54.054493Z", + "shell.execute_reply": "2023-08-22T06:59:54.054110Z" } }, "outputs": [], @@ -61,10 +61,10 @@ "id": "c4b3398b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:12.284057Z", - "iopub.status.busy": "2023-08-21T02:29:12.283895Z", - "iopub.status.idle": "2023-08-21T02:29:14.091024Z", - "shell.execute_reply": "2023-08-21T02:29:14.090709Z" + "iopub.execute_input": "2023-08-22T06:59:54.056594Z", + "iopub.status.busy": "2023-08-22T06:59:54.056412Z", + "iopub.status.idle": "2023-08-22T06:59:56.004484Z", + "shell.execute_reply": "2023-08-22T06:59:56.004082Z" }, "lines_to_next_cell": 0 }, @@ -73,11 +73,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: l0bnb in /Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages (1.0.0)\r\n", - "Requirement already satisfied: numpy>=1.18.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages (from l0bnb) (1.24.2)\r\n", - "Requirement already satisfied: scipy>=1.4.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages (from l0bnb) (1.11.1)\r\n", - "Requirement already satisfied: numba>=0.53.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages (from l0bnb) (0.57.1)\r\n", - "Requirement already satisfied: llvmlite<0.41,>=0.40.0dev0 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages (from numba>=0.53.1->l0bnb) (0.40.1)\r\n" + "Requirement already satisfied: l0bnb in /Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages (1.0.0)\r\n", + "Requirement already satisfied: numpy>=1.18.1 in /Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages (from l0bnb) (1.24.2)\r\n", + "Requirement already satisfied: scipy>=1.4.1 in /Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages (from l0bnb) (1.11.1)\r\n", + "Requirement already satisfied: numba>=0.53.1 in /Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages (from l0bnb) (0.57.1)\r\n", + "Requirement already satisfied: llvmlite<0.41,>=0.40.0dev0 in /Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages (from numba>=0.53.1->l0bnb) (0.40.1)\r\n" ] } ], @@ -125,10 +125,10 @@ "id": "18d03122", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:14.092873Z", - "iopub.status.busy": "2023-08-21T02:29:14.092763Z", - "iopub.status.idle": "2023-08-21T02:29:14.099984Z", - "shell.execute_reply": "2023-08-21T02:29:14.099726Z" + "iopub.execute_input": "2023-08-22T06:59:56.006401Z", + "iopub.status.busy": "2023-08-22T06:59:56.006276Z", + "iopub.status.idle": "2023-08-22T06:59:56.013280Z", + "shell.execute_reply": "2023-08-22T06:59:56.012957Z" } }, "outputs": [ @@ -164,10 +164,10 @@ "id": "87a4ba00", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:14.101494Z", - "iopub.status.busy": "2023-08-21T02:29:14.101409Z", - "iopub.status.idle": "2023-08-21T02:29:14.104265Z", - "shell.execute_reply": "2023-08-21T02:29:14.104009Z" + "iopub.execute_input": "2023-08-22T06:59:56.014992Z", + "iopub.status.busy": "2023-08-22T06:59:56.014883Z", + "iopub.status.idle": "2023-08-22T06:59:56.017852Z", + "shell.execute_reply": "2023-08-22T06:59:56.017594Z" }, "lines_to_next_cell": 2 }, @@ -205,10 +205,10 @@ "id": "97d6b69c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:14.105644Z", - "iopub.status.busy": "2023-08-21T02:29:14.105549Z", - "iopub.status.idle": "2023-08-21T02:29:14.107583Z", - "shell.execute_reply": "2023-08-21T02:29:14.107310Z" + "iopub.execute_input": "2023-08-22T06:59:56.019330Z", + "iopub.status.busy": "2023-08-22T06:59:56.019227Z", + "iopub.status.idle": "2023-08-22T06:59:56.021261Z", + "shell.execute_reply": "2023-08-22T06:59:56.020923Z" }, "lines_to_next_cell": 0 }, @@ -237,10 +237,10 @@ "id": "2575e116", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:14.109006Z", - "iopub.status.busy": "2023-08-21T02:29:14.108924Z", - "iopub.status.idle": "2023-08-21T02:29:14.129547Z", - "shell.execute_reply": "2023-08-21T02:29:14.129253Z" + "iopub.execute_input": "2023-08-22T06:59:56.023126Z", + "iopub.status.busy": "2023-08-22T06:59:56.023002Z", + "iopub.status.idle": "2023-08-22T06:59:56.043909Z", + "shell.execute_reply": "2023-08-22T06:59:56.043598Z" } }, "outputs": [], @@ -265,10 +265,10 @@ "id": "4cfae1c0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:14.131136Z", - "iopub.status.busy": "2023-08-21T02:29:14.131050Z", - "iopub.status.idle": "2023-08-21T02:29:14.132859Z", - "shell.execute_reply": "2023-08-21T02:29:14.132610Z" + "iopub.execute_input": "2023-08-22T06:59:56.046001Z", + "iopub.status.busy": "2023-08-22T06:59:56.045882Z", + "iopub.status.idle": "2023-08-22T06:59:56.047752Z", + "shell.execute_reply": "2023-08-22T06:59:56.047459Z" }, "lines_to_next_cell": 0 }, @@ -303,10 +303,10 @@ "id": "98b62676", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:14.134396Z", - "iopub.status.busy": "2023-08-21T02:29:14.134312Z", - "iopub.status.idle": "2023-08-21T02:29:14.136156Z", - "shell.execute_reply": "2023-08-21T02:29:14.135905Z" + "iopub.execute_input": "2023-08-22T06:59:56.049394Z", + "iopub.status.busy": "2023-08-22T06:59:56.049284Z", + "iopub.status.idle": "2023-08-22T06:59:56.051180Z", + "shell.execute_reply": "2023-08-22T06:59:56.050841Z" } }, "outputs": [], @@ -334,10 +334,10 @@ "id": "3d1bd25a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:14.137540Z", - "iopub.status.busy": "2023-08-21T02:29:14.137462Z", - "iopub.status.idle": "2023-08-21T02:29:15.145790Z", - "shell.execute_reply": "2023-08-21T02:29:15.145505Z" + "iopub.execute_input": "2023-08-22T06:59:56.053484Z", + "iopub.status.busy": "2023-08-22T06:59:56.053197Z", + "iopub.status.idle": "2023-08-22T06:59:57.116576Z", + "shell.execute_reply": "2023-08-22T06:59:57.116202Z" } }, "outputs": [ @@ -391,10 +391,10 @@ "id": "b25dfa6c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:15.147492Z", - "iopub.status.busy": "2023-08-21T02:29:15.147368Z", - "iopub.status.idle": "2023-08-21T02:29:15.796510Z", - "shell.execute_reply": "2023-08-21T02:29:15.796218Z" + "iopub.execute_input": "2023-08-22T06:59:57.118521Z", + "iopub.status.busy": "2023-08-22T06:59:57.118381Z", + "iopub.status.idle": "2023-08-22T06:59:57.757333Z", + "shell.execute_reply": "2023-08-22T06:59:57.756933Z" } }, "outputs": [ @@ -457,10 +457,10 @@ "id": "0ef3f82d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:15.798342Z", - "iopub.status.busy": "2023-08-21T02:29:15.798201Z", - "iopub.status.idle": "2023-08-21T02:29:15.800265Z", - "shell.execute_reply": "2023-08-21T02:29:15.799952Z" + "iopub.execute_input": "2023-08-22T06:59:57.759379Z", + "iopub.status.busy": "2023-08-22T06:59:57.759234Z", + "iopub.status.idle": "2023-08-22T06:59:57.761149Z", + "shell.execute_reply": "2023-08-22T06:59:57.760820Z" } }, "outputs": [], @@ -485,10 +485,10 @@ "id": "d4a8cf16", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:15.802048Z", - "iopub.status.busy": "2023-08-21T02:29:15.801932Z", - "iopub.status.idle": "2023-08-21T02:29:16.552146Z", - "shell.execute_reply": "2023-08-21T02:29:16.550354Z" + "iopub.execute_input": "2023-08-22T06:59:57.762826Z", + "iopub.status.busy": "2023-08-22T06:59:57.762724Z", + "iopub.status.idle": "2023-08-22T06:59:58.384792Z", + "shell.execute_reply": "2023-08-22T06:59:58.384441Z" }, "lines_to_next_cell": 2 }, @@ -530,10 +530,10 @@ "id": "ce40a9ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:16.556568Z", - "iopub.status.busy": "2023-08-21T02:29:16.555939Z", - "iopub.status.idle": "2023-08-21T02:29:16.866266Z", - "shell.execute_reply": "2023-08-21T02:29:16.865469Z" + "iopub.execute_input": "2023-08-22T06:59:58.386657Z", + "iopub.status.busy": "2023-08-22T06:59:58.386521Z", + "iopub.status.idle": "2023-08-22T06:59:58.504180Z", + "shell.execute_reply": "2023-08-22T06:59:58.503223Z" }, "lines_to_next_cell": 0 }, @@ -599,10 +599,10 @@ "id": "c7416ff6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:16.869059Z", - "iopub.status.busy": "2023-08-21T02:29:16.868880Z", - "iopub.status.idle": "2023-08-21T02:29:20.216833Z", - "shell.execute_reply": "2023-08-21T02:29:20.216545Z" + "iopub.execute_input": "2023-08-22T06:59:58.509147Z", + "iopub.status.busy": "2023-08-22T06:59:58.508897Z", + "iopub.status.idle": "2023-08-22T07:00:01.677521Z", + "shell.execute_reply": "2023-08-22T07:00:01.677198Z" }, "lines_to_next_cell": 0 }, @@ -653,10 +653,10 @@ "id": "a2ae089b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:20.218513Z", - "iopub.status.busy": "2023-08-21T02:29:20.218396Z", - "iopub.status.idle": "2023-08-21T02:29:20.221477Z", - "shell.execute_reply": "2023-08-21T02:29:20.221172Z" + "iopub.execute_input": "2023-08-22T07:00:01.679228Z", + "iopub.status.busy": "2023-08-22T07:00:01.679111Z", + "iopub.status.idle": "2023-08-22T07:00:01.681995Z", + "shell.execute_reply": "2023-08-22T07:00:01.681709Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "1681a9db", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:20.223098Z", - "iopub.status.busy": "2023-08-21T02:29:20.223012Z", - "iopub.status.idle": "2023-08-21T02:29:20.308656Z", - "shell.execute_reply": "2023-08-21T02:29:20.308356Z" + "iopub.execute_input": "2023-08-22T07:00:01.683590Z", + "iopub.status.busy": "2023-08-22T07:00:01.683477Z", + "iopub.status.idle": "2023-08-22T07:00:01.759136Z", + "shell.execute_reply": "2023-08-22T07:00:01.758804Z" } }, "outputs": [ @@ -741,10 +741,10 @@ "id": "5764b0ba", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:20.310366Z", - "iopub.status.busy": "2023-08-21T02:29:20.310242Z", - "iopub.status.idle": "2023-08-21T02:29:20.927992Z", - "shell.execute_reply": "2023-08-21T02:29:20.927702Z" + "iopub.execute_input": "2023-08-22T07:00:01.760831Z", + "iopub.status.busy": "2023-08-22T07:00:01.760716Z", + "iopub.status.idle": "2023-08-22T07:00:02.384829Z", + "shell.execute_reply": "2023-08-22T07:00:02.384507Z" }, "lines_to_next_cell": 0 }, @@ -775,10 +775,10 @@ "id": "d76f3d3a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:20.929777Z", - "iopub.status.busy": "2023-08-21T02:29:20.929678Z", - "iopub.status.idle": "2023-08-21T02:29:21.021186Z", - "shell.execute_reply": "2023-08-21T02:29:21.020858Z" + "iopub.execute_input": "2023-08-22T07:00:02.387276Z", + "iopub.status.busy": "2023-08-22T07:00:02.386879Z", + "iopub.status.idle": "2023-08-22T07:00:02.468255Z", + "shell.execute_reply": "2023-08-22T07:00:02.467684Z" }, "lines_to_next_cell": 2 }, @@ -828,10 +828,10 @@ "id": "ed25c2d4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:21.022941Z", - "iopub.status.busy": "2023-08-21T02:29:21.022836Z", - "iopub.status.idle": "2023-08-21T02:29:21.043228Z", - "shell.execute_reply": "2023-08-21T02:29:21.042926Z" + "iopub.execute_input": "2023-08-22T07:00:02.470331Z", + "iopub.status.busy": "2023-08-22T07:00:02.470178Z", + "iopub.status.idle": "2023-08-22T07:00:02.491378Z", + "shell.execute_reply": "2023-08-22T07:00:02.491048Z" }, "lines_to_next_cell": 0 }, @@ -857,10 +857,10 @@ "id": "31e99e02", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:21.044846Z", - "iopub.status.busy": "2023-08-21T02:29:21.044745Z", - "iopub.status.idle": "2023-08-21T02:29:23.376539Z", - "shell.execute_reply": "2023-08-21T02:29:23.376256Z" + "iopub.execute_input": "2023-08-22T07:00:02.493549Z", + "iopub.status.busy": "2023-08-22T07:00:02.493371Z", + "iopub.status.idle": "2023-08-22T07:00:07.838494Z", + "shell.execute_reply": "2023-08-22T07:00:07.838064Z" } }, "outputs": [ @@ -912,10 +912,10 @@ "id": "214d0f23", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.378603Z", - "iopub.status.busy": "2023-08-21T02:29:23.378467Z", - "iopub.status.idle": "2023-08-21T02:29:23.380970Z", - "shell.execute_reply": "2023-08-21T02:29:23.380691Z" + "iopub.execute_input": "2023-08-22T07:00:07.840443Z", + "iopub.status.busy": "2023-08-22T07:00:07.840298Z", + "iopub.status.idle": "2023-08-22T07:00:07.842812Z", + "shell.execute_reply": "2023-08-22T07:00:07.842557Z" }, "lines_to_next_cell": 0 }, @@ -977,10 +977,10 @@ "id": "ca7469fb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.382549Z", - "iopub.status.busy": "2023-08-21T02:29:23.382432Z", - "iopub.status.idle": "2023-08-21T02:29:23.450752Z", - "shell.execute_reply": "2023-08-21T02:29:23.450471Z" + "iopub.execute_input": "2023-08-22T07:00:07.844529Z", + "iopub.status.busy": "2023-08-22T07:00:07.844419Z", + "iopub.status.idle": "2023-08-22T07:00:07.900374Z", + "shell.execute_reply": "2023-08-22T07:00:07.899998Z" }, "lines_to_next_cell": 0 }, @@ -989,405 +989,405 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428165.36474803, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428165.36474803, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428069.135193564, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428069.135193564, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427947.709570706, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427947.709570706, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427794.49147929, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427794.49147929, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427601.15801401, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427601.15801401, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427357.208145335, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427357.208145335, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427049.39312406, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427049.39312406, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426660.99818401, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426660.99818401, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426170.936871, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426170.936871, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64425552.60935727, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64425552.60935727, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64424772.46361481, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64424772.46361481, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64423788.18271286, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64423788.18271286, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64422546.402046196, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64422546.402046196, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64420979.836119056, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64420979.836119056, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64419003.66458898, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64419003.66458898, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64416510.99045885, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64416510.99045885, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64413367.138336174, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64413367.138336174, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64409402.50628651, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64409402.50628651, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64404403.61988451, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64404403.61988451, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64398101.96098537, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64398101.96098537, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64390160.05690916, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64390160.05690916, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380154.22050254, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380154.22050254, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64367553.23368757, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64367553.23368757, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64351692.17811265, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64351692.17811265, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64331740.55708714, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64331740.55708714, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64306663.85815487, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64306663.85815487, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64275177.83204634, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64275177.83204634, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64235695.09903011, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64235695.09903011, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64186264.367964305, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64186264.367964305, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64124503.75014188, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64124503.75014188, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64047531.61120446, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64047531.61120446, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63951901.41718618, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63951901.41718618, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63833551.374737374, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63833551.374737374, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63687785.48493876, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63687785.48493876, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63509309.685659595, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63509309.685659595, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63292354.02159835, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63292354.02159835, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63030916.89990266, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63030916.89990266, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62719166.29703928, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62719166.29703928, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62352019.354438685, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62352019.354438685, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61925889.875772476, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61925889.875772476, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61439539.89859062, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61439539.89859062, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60894903.039219804, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60894903.039219804, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60297684.607476555, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60297684.607476555, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59657521.16598571, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59657521.16598571, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58987535.05051082, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58987535.05051082, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58303257.30893663, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58303257.30893663, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57621079.35589412, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57621079.35589412, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56956552.362989165, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56956552.362989165, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56322906.14367991, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56322906.14367991, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55730077.752803415, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55730077.752803415, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55184365.56435659, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55184365.56435659, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54688640.34364891, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54688640.34364891, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54242923.97107168, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54242923.97107168, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53845116.92275897, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53845116.92275897, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53491699.68250863, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53491699.68250863, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53178310.76477921, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53178310.76477921, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52900177.09233121, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52900177.09233121, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52652419.277090184, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52652419.277090184, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52430270.98847021, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52430270.98847021, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52229246.49376922, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52229246.49376922, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52045276.251295805, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52045276.251295805, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51874817.10761593, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51874817.10761593, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51714935.480955906, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51714935.480955906, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51563358.53546297, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51563358.53546297, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51418487.867063135, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51418487.867063135, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51279371.6204245, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51279371.6204245, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51145634.32609803, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51145634.32609803, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51017369.002990715, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51017369.002990715, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50895002.06601913, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50895002.06601913, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50779146.50047491, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50779146.50047491, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50670461.07683641, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50670461.07683641, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50569532.273268215, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50569532.273268215, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50476790.981010474, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50476790.981010474, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50392468.80539254, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50392468.80539254, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50316590.69087247, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50316590.69087247, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50248994.15213543, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50248994.15213543, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50189362.60450393, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50189362.60450393, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50137261.69126286, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50137261.69126286, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50092171.83247456, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50092171.83247456, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50053515.0816231, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50053515.0816231, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50020677.61213055, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50020677.61213055, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49993029.950182974, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49993029.950182974, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49969946.08142715, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49969946.08142715, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49950821.12032734, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49950821.12032734, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49935086.375795275, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49935086.375795275, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49922220.65542218, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49922220.65542218, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49911757.23721766, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49911757.23721766, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49903286.65921827, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49903286.65921827, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49896456.01861009, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49896456.01861009, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49890965.72520982, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49890965.72520982, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49886564.66025478, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49886564.66025478, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49883044.54819732, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49883044.54819732, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49880234.147845834, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49880234.147845834, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49877993.670362815, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49877993.670362815, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49876209.66553557, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49876209.66553557, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49874790.493499264, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49874790.493499264, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49873662.41408341, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49873662.41408341, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872766.272819825, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872766.272819825, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872054.73300109, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872054.73300109, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49871489.989638604, tolerance: 12885.7065737425\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49871489.989638604, tolerance: 12885.7065737425\n", " model = cd_fast.enet_coordinate_descent_gram(\n" ] }, @@ -1444,10 +1444,10 @@ "id": "a5b4b3a7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.452873Z", - "iopub.status.busy": "2023-08-21T02:29:23.452757Z", - "iopub.status.idle": "2023-08-21T02:29:23.462335Z", - "shell.execute_reply": "2023-08-21T02:29:23.462029Z" + "iopub.execute_input": "2023-08-22T07:00:07.902654Z", + "iopub.status.busy": "2023-08-22T07:00:07.902513Z", + "iopub.status.idle": "2023-08-22T07:00:07.912659Z", + "shell.execute_reply": "2023-08-22T07:00:07.912286Z" } }, "outputs": [ @@ -1866,10 +1866,10 @@ "id": "ffdcd0c6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.463956Z", - "iopub.status.busy": "2023-08-21T02:29:23.463846Z", - "iopub.status.idle": "2023-08-21T02:29:23.670285Z", - "shell.execute_reply": "2023-08-21T02:29:23.669949Z" + "iopub.execute_input": "2023-08-22T07:00:07.914460Z", + "iopub.status.busy": "2023-08-22T07:00:07.914335Z", + "iopub.status.idle": "2023-08-22T07:00:08.105311Z", + "shell.execute_reply": "2023-08-22T07:00:08.104857Z" }, "lines_to_next_cell": 0 }, @@ -1911,10 +1911,10 @@ "id": "e60d63d3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.671975Z", - "iopub.status.busy": "2023-08-21T02:29:23.671852Z", - "iopub.status.idle": "2023-08-21T02:29:23.674849Z", - "shell.execute_reply": "2023-08-21T02:29:23.674590Z" + "iopub.execute_input": "2023-08-22T07:00:08.107307Z", + "iopub.status.busy": "2023-08-22T07:00:08.107179Z", + "iopub.status.idle": "2023-08-22T07:00:08.110477Z", + "shell.execute_reply": "2023-08-22T07:00:08.110049Z" } }, "outputs": [ @@ -1968,10 +1968,10 @@ "id": "a2253f89", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.676319Z", - "iopub.status.busy": "2023-08-21T02:29:23.676213Z", - "iopub.status.idle": "2023-08-21T02:29:23.678423Z", - "shell.execute_reply": "2023-08-21T02:29:23.678191Z" + "iopub.execute_input": "2023-08-22T07:00:08.112116Z", + "iopub.status.busy": "2023-08-22T07:00:08.112018Z", + "iopub.status.idle": "2023-08-22T07:00:08.114728Z", + "shell.execute_reply": "2023-08-22T07:00:08.114379Z" } }, "outputs": [ @@ -2006,10 +2006,10 @@ "id": "aa10fcf4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.679982Z", - "iopub.status.busy": "2023-08-21T02:29:23.679875Z", - "iopub.status.idle": "2023-08-21T02:29:23.682329Z", - "shell.execute_reply": "2023-08-21T02:29:23.682074Z" + "iopub.execute_input": "2023-08-22T07:00:08.116886Z", + "iopub.status.busy": "2023-08-22T07:00:08.116732Z", + "iopub.status.idle": "2023-08-22T07:00:08.119361Z", + "shell.execute_reply": "2023-08-22T07:00:08.119014Z" }, "lines_to_next_cell": 0 }, @@ -2047,10 +2047,10 @@ "id": "f5bc4121", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.683856Z", - "iopub.status.busy": "2023-08-21T02:29:23.683756Z", - "iopub.status.idle": "2023-08-21T02:29:23.696250Z", - "shell.execute_reply": "2023-08-21T02:29:23.695956Z" + "iopub.execute_input": "2023-08-22T07:00:08.121086Z", + "iopub.status.busy": "2023-08-22T07:00:08.120969Z", + "iopub.status.idle": "2023-08-22T07:00:08.133376Z", + "shell.execute_reply": "2023-08-22T07:00:08.133011Z" } }, "outputs": [ @@ -2058,7 +2058,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.446e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.446e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -2100,10 +2100,10 @@ "id": "e4018437", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.697819Z", - "iopub.status.busy": "2023-08-21T02:29:23.697718Z", - "iopub.status.idle": "2023-08-21T02:29:23.700233Z", - "shell.execute_reply": "2023-08-21T02:29:23.699974Z" + "iopub.execute_input": "2023-08-22T07:00:08.135390Z", + "iopub.status.busy": "2023-08-22T07:00:08.135249Z", + "iopub.status.idle": "2023-08-22T07:00:08.138028Z", + "shell.execute_reply": "2023-08-22T07:00:08.137669Z" }, "lines_to_next_cell": 0 }, @@ -2147,10 +2147,10 @@ "id": "aaa73183", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.701707Z", - "iopub.status.busy": "2023-08-21T02:29:23.701624Z", - "iopub.status.idle": "2023-08-21T02:29:23.709254Z", - "shell.execute_reply": "2023-08-21T02:29:23.708916Z" + "iopub.execute_input": "2023-08-22T07:00:08.140104Z", + "iopub.status.busy": "2023-08-22T07:00:08.139927Z", + "iopub.status.idle": "2023-08-22T07:00:08.147255Z", + "shell.execute_reply": "2023-08-22T07:00:08.146900Z" } }, "outputs": [ @@ -2158,7 +2158,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -2205,10 +2205,10 @@ "id": "8f95689e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.711112Z", - "iopub.status.busy": "2023-08-21T02:29:23.710959Z", - "iopub.status.idle": "2023-08-21T02:29:23.718893Z", - "shell.execute_reply": "2023-08-21T02:29:23.718567Z" + "iopub.execute_input": "2023-08-22T07:00:08.149814Z", + "iopub.status.busy": "2023-08-22T07:00:08.149544Z", + "iopub.status.idle": "2023-08-22T07:00:08.157631Z", + "shell.execute_reply": "2023-08-22T07:00:08.157328Z" }, "lines_to_next_cell": 0 }, @@ -2217,7 +2217,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -2262,10 +2262,10 @@ "id": "725a3200", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:23.720467Z", - "iopub.status.busy": "2023-08-21T02:29:23.720361Z", - "iopub.status.idle": "2023-08-21T02:29:24.185363Z", - "shell.execute_reply": "2023-08-21T02:29:24.185015Z" + "iopub.execute_input": "2023-08-22T07:00:08.159712Z", + "iopub.status.busy": "2023-08-22T07:00:08.159508Z", + "iopub.status.idle": "2023-08-22T07:00:08.559312Z", + "shell.execute_reply": "2023-08-22T07:00:08.558977Z" } }, "outputs": [ @@ -2273,207 +2273,207 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.131e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.131e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.081e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.081e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.744e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.744e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.494e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.494e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.968e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.968e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.704e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.704e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.448e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.448e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.204e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.204e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.769e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.769e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.581e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.581e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.412e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.412e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.261e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.261e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.127e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.127e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.008e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.008e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.900e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.900e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.803e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.803e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.714e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.714e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.632e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.632e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.554e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.554e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.480e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.480e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.409e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.409e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.342e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.342e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.214e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.214e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.154e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.154e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.097e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.097e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.043e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.043e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.991e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.991e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.943e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.943e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.898e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.898e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.856e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.856e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.817e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.817e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.780e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.780e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.746e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.746e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.715e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.715e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.687e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.687e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.661e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.661e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.637e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.637e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.616e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.616e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.596e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.596e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.579e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.579e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.563e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.563e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.550e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.550e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.538e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.538e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.528e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.528e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.519e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.519e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.512e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.512e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.506e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.506e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.500e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.500e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.496e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.496e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.493e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.493e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.490e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.490e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.485e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.485e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.482e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.482e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -2519,10 +2519,10 @@ "id": "823982cc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:24.187182Z", - "iopub.status.busy": "2023-08-21T02:29:24.187049Z", - "iopub.status.idle": "2023-08-21T02:29:27.299737Z", - "shell.execute_reply": "2023-08-21T02:29:27.299435Z" + "iopub.execute_input": "2023-08-22T07:00:08.561312Z", + "iopub.status.busy": "2023-08-22T07:00:08.561182Z", + "iopub.status.idle": "2023-08-22T07:00:11.194918Z", + "shell.execute_reply": "2023-08-22T07:00:11.194585Z" }, "lines_to_next_cell": 0 }, @@ -2531,1007 +2531,1007 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -3577,10 +3577,10 @@ "id": "b1b7d3b4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:27.301365Z", - "iopub.status.busy": "2023-08-21T02:29:27.301252Z", - "iopub.status.idle": "2023-08-21T02:29:27.401673Z", - "shell.execute_reply": "2023-08-21T02:29:27.401251Z" + "iopub.execute_input": "2023-08-22T07:00:11.196745Z", + "iopub.status.busy": "2023-08-22T07:00:11.196604Z", + "iopub.status.idle": "2023-08-22T07:00:11.287223Z", + "shell.execute_reply": "2023-08-22T07:00:11.286902Z" } }, "outputs": [ @@ -3621,10 +3621,10 @@ "id": "59a69421", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:27.403382Z", - "iopub.status.busy": "2023-08-21T02:29:27.403269Z", - "iopub.status.idle": "2023-08-21T02:29:30.557588Z", - "shell.execute_reply": "2023-08-21T02:29:30.557217Z" + "iopub.execute_input": "2023-08-22T07:00:11.289077Z", + "iopub.status.busy": "2023-08-22T07:00:11.288949Z", + "iopub.status.idle": "2023-08-22T07:00:13.927873Z", + "shell.execute_reply": "2023-08-22T07:00:13.927443Z" } }, "outputs": [ @@ -3632,1007 +3632,1007 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -4711,10 +4711,10 @@ "id": "572b14c3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:30.559540Z", - "iopub.status.busy": "2023-08-21T02:29:30.559406Z", - "iopub.status.idle": "2023-08-21T02:29:30.661941Z", - "shell.execute_reply": "2023-08-21T02:29:30.660478Z" + "iopub.execute_input": "2023-08-22T07:00:13.930022Z", + "iopub.status.busy": "2023-08-22T07:00:13.929865Z", + "iopub.status.idle": "2023-08-22T07:00:14.018201Z", + "shell.execute_reply": "2023-08-22T07:00:14.017805Z" }, "lines_to_next_cell": 2 }, @@ -4760,10 +4760,10 @@ "id": "a6129b36", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:30.663816Z", - "iopub.status.busy": "2023-08-21T02:29:30.663686Z", - "iopub.status.idle": "2023-08-21T02:29:31.013347Z", - "shell.execute_reply": "2023-08-21T02:29:31.012943Z" + "iopub.execute_input": "2023-08-22T07:00:14.020355Z", + "iopub.status.busy": "2023-08-22T07:00:14.020186Z", + "iopub.status.idle": "2023-08-22T07:00:14.305398Z", + "shell.execute_reply": "2023-08-22T07:00:14.305076Z" } }, "outputs": [ @@ -4771,2007 +4771,2007 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795326.355502333, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795326.355502333, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795268.885511458, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795268.885511458, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795196.367825005, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795196.367825005, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795104.862821113, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795104.862821113, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794989.399687696, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794989.399687696, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794843.706650957, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794843.706650957, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794659.87071198, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794659.87071198, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794427.908521358, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794427.908521358, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794135.22526347, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794135.22526347, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793765.932449568, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793765.932449568, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793299.98803079, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793299.98803079, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18792712.112872534, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18792712.112872534, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791970.425932087, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791970.425932087, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791034.72591697, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791034.72591697, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18789854.32913581, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18789854.32913581, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18788365.350956466, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18788365.350956466, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18786487.290938053, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18786487.290938053, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18784118.748442672, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18784118.748442672, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18781132.05553399, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18781132.05553399, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18777366.566605024, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18777366.566605024, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18772620.289297033, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18772620.289297033, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18766639.479676694, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18766639.479676694, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18759105.758860495, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18759105.758860495, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18749620.243803147, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18749620.243803147, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18737684.132153213, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18737684.132153213, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18722675.157982755, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18722675.157982755, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18703819.37168406, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18703819.37168406, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18680157.84067929, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18680157.84067929, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18650508.189617783, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18650508.189617783, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18613421.503628485, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18613421.503628485, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18567136.14871325, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18567136.14871325, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18509531.699850053, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18509531.699850053, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18438088.608600505, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18438088.608600505, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18349862.649110064, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18349862.649110064, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18241487.557216965, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18241487.557216965, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18109224.25083878, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18109224.25083878, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17949079.523028806, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17949079.523028806, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17757018.994714484, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17757018.994714484, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17529294.98190815, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17529294.98190815, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17262895.457700975, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17262895.457700975, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16956091.882983487, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16956091.882983487, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16609021.736273043, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16609021.736273043, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16224194.650997939, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16224194.650997939, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15806778.142363884, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15806778.142363884, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15364525.127389485, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15364525.127389485, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14907268.75187378, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14907268.75187378, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14446023.624531085, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14446023.624531085, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13991857.160644894, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13991857.160644894, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13554773.727504015, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13554773.727504015, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13142847.182203237, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13142847.182203237, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12761747.456957739, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12761747.456957739, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12414679.232309299, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12414679.232309299, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12102642.724649917, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12102642.724649917, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11824874.692517474, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11824874.692517474, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11579334.50630629, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11579334.50630629, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11363143.416383019, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11363143.416383019, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11172936.696242273, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11172936.696242273, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11005127.92643167, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11005127.92643167, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10856105.032984463, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10856105.032984463, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10722381.625233045, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10722381.625233045, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10600721.735570516, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10600721.735570516, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10488247.552619573, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10488247.552619573, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10382531.68105097, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10382531.68105097, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10281669.161078632, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10281669.161078632, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10184320.545404715, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10184320.545404715, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089716.55059902, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089716.55059902, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9997617.850835908, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9997617.850835908, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9908230.155360885, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9908230.155360885, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9822083.085401118, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9822083.085401118, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9739888.930170696, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9739888.930170696, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9662401.666184625, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9662401.666184625, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9590296.226307327, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9590296.226307327, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9524082.854699288, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9524082.854699288, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9464062.902306747, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9464062.902306747, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9410323.196208755, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9410323.196208755, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9362759.024991764, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9362759.024991764, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9321112.753117379, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9321112.753117379, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9285016.290065145, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9285016.290065145, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9254029.627395952, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9254029.627395952, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9227672.214767914, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9227672.214767914, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9205447.27460862, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9205447.27460862, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9186860.578098293, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9186860.578098293, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9171435.130133288, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9171435.130133288, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9158722.527650403, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9158722.527650403, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9148311.191396464, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9148311.191396464, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9139831.50202173, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9139831.50202173, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9132958.012055235, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9132958.012055235, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9127409.145408802, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9127409.145408802, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9122944.972944392, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9122944.972944392, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9119363.705526328, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9119363.705526328, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9116497.490587894, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9116497.490587894, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9114207.980834428, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9114207.980834428, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9112382.008592516, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9112382.008592516, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9110927.575648237, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9110927.575648237, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9109770.269829819, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9109770.269829819, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108850.148759764, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108850.148759764, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108119.08491204, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108119.08491204, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107538.538969103, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107538.538969103, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107077.714962065, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107077.714962065, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9106712.046135923, tolerance: 3759.109166869193\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9106712.046135923, tolerance: 3759.109166869193\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005651.632865302, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005651.632865302, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005578.608102243, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005578.608102243, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005486.463074774, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005486.463074774, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005370.192059726, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005370.192059726, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005223.47917251, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005223.47917251, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005038.355660334, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005038.355660334, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004804.76767336, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004804.76767336, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004510.03120046, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004510.03120046, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004138.144828446, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004138.144828446, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003668.923421204, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003668.923421204, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003076.906345215, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003076.906345215, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21002329.98203154, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21002329.98203154, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21001387.655909717, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21001387.655909717, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21000198.8704182, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21000198.8704182, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20998699.26312138, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20998699.26312138, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20996807.72107362, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20996807.72107362, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20994422.05552329, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20994422.05552329, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20991413.57989597, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20991413.57989597, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20987620.324921425, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20987620.324921425, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20982838.567338496, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20982838.567338496, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20976812.283196613, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20976812.283196613, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20969220.065253027, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20969220.065253027, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20959658.970863715, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20959658.970863715, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20947624.701018073, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20947624.701018073, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20932487.468798272, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20932487.468798272, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20913462.923603535, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20913462.923603535, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20889577.599545892, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20889577.599545892, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20859628.61984418, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20859628.61984418, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20822137.913488373, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20822137.913488373, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20775302.126054227, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20775302.126054227, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20716940.917180095, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20716940.917180095, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20644448.64953633, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20644448.64953633, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20554757.795455974, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20554757.795455974, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20444326.815649558, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20444326.815649558, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20309170.5956441, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20309170.5956441, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20144956.94257016, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20144956.94257016, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19947196.308887925, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19947196.308887925, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19711550.604615457, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19711550.604615457, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19434276.168588594, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19434276.168588594, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19112791.023677077, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19112791.023677077, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18746315.49762964, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18746315.49762964, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18336483.416578818, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18336483.416578818, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17887774.82963546, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17887774.82963546, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17407607.14883928, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17407607.14883928, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16905965.499829993, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16905965.499829993, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16394560.80209675, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16394560.80209675, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15885645.94315279, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15885645.94315279, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15390736.734407002, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15390736.734407002, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14919517.25785277, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14919517.25785277, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14479140.715843389, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14479140.715843389, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14074002.01810337, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14074002.01810337, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13705921.512677444, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13705921.512677444, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13374594.126102015, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13374594.126102015, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13078142.079861483, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13078142.079861483, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12813645.639316088, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12813645.639316088, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12577583.791150972, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12577583.791150972, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12366168.387483226, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12366168.387483226, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12175587.27845306, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12175587.27845306, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12002182.958268248, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12002182.958268248, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11842589.470659975, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11842589.470659975, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11693840.031875866, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11693840.031875866, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11553447.60800361, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11553447.60800361, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11419454.0438313, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11419454.0438313, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11290441.388440857, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11290441.388440857, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11165501.742342338, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11165501.742342338, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11044168.420816425, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11044168.420816425, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10926319.289729377, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10926319.289729377, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10812069.210340973, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10812069.210340973, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10701669.403435929, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10701669.403435929, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10595426.714498514, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10595426.714498514, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10493648.013477515, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10493648.013477515, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10396608.203056702, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10396608.203056702, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10304536.713966034, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10304536.713966034, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10217616.440012153, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10217616.440012153, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10135989.092876721, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10135989.092876721, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10059761.060749074, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10059761.060749074, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9989004.697692012, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9989004.697692012, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9923752.620593688, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9923752.620593688, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9863986.795334544, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9863986.795334544, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9809627.884194935, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9809627.884194935, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9760531.052715844, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9760531.052715844, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9716491.487344079, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9716491.487344079, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9677258.06531545, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9677258.06531545, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9642549.951165989, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9642549.951165989, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9612070.387835175, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9612070.387835175, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9585514.488134583, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9585514.488134583, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9562571.500908714, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9562571.500908714, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9542924.549681038, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9542924.549681038, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9526251.156759001, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9526251.156759001, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9512226.472533092, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9512226.472533092, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9500529.267319627, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9500529.267319627, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9490849.431706948, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9490849.431706948, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9482895.334901826, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9482895.334901826, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9476399.71781569, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9476399.71781569, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471123.439398324, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471123.439398324, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9466857.004635958, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9466857.004635958, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9463420.20844639, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9463420.20844639, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9460660.409301298, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9460660.409301298, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9458449.957484353, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9458449.957484353, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9456683.22035802, tolerance: 4201.186103419478\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9456683.22035802, tolerance: 4201.186103419478\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331946.25629055, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331946.25629055, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331864.018678214, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331864.018678214, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331760.248581372, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331760.248581372, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331629.308755428, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331629.308755428, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331464.086506005, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331464.086506005, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331255.607747704, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331255.607747704, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330992.550247405, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330992.550247405, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330660.62979839, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330660.62979839, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330241.82628314, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330241.82628314, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329713.40806704, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329713.40806704, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329046.702501133, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329046.702501133, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22328205.546983715, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22328205.546983715, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22327144.338416774, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22327144.338416774, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22325805.578253012, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22325805.578253012, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22324116.784799173, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22324116.784799173, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22321986.613041975, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22321986.613041975, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22319299.9839329, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22319299.9839329, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22315911.97874348, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22315911.97874348, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22311640.198869713, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22311640.198869713, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22306255.226839963, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22306255.226839963, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22299468.750693016, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22299468.750693016, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22290918.833475478, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22290918.833475478, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22280151.72747749, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22280151.72747749, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22266599.559077755, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22266599.559077755, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22249553.162800502, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22249553.162800502, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22228129.35292585, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22228129.35292585, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22201232.036903117, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22201232.036903117, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22167506.872833706, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22167506.872833706, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22125289.76574775, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22125289.76574775, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22072550.542125095, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22072550.542125095, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22006834.845984127, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22006834.845984127, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21925209.906269174, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21925209.906269174, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21824223.56629905, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21824223.56629905, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21699890.94922881, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21699890.94922881, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21547729.124614064, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21547729.124614064, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21362866.213577304, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21362866.213577304, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21140255.446179498, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21140255.446179498, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20875023.13975618, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20875023.13975618, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20562967.32341789, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20562967.32341789, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201195.56502676, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201195.56502676, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19788844.32939185, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19788844.32939185, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19327763.89751004, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19327763.89751004, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18823001.04313301, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18823001.04313301, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18282896.08461045, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18282896.08461045, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17718660.4886989, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17718660.4886989, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17143422.40324079, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17143422.40324079, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16570887.230051238, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16570887.230051238, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16013892.090309372, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16013892.090309372, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15483171.861886727, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15483171.861886727, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14986579.129588084, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14986579.129588084, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14528848.289413737, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14528848.289413737, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14111836.239774454, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14111836.239774454, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13735069.935277399, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13735069.935277399, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13396407.639332836, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13396407.639332836, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13092660.916831579, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13092660.916831579, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12820093.900344713, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12820093.900344713, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12574781.90922219, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12574781.90922219, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12352853.175078167, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12352853.175078167, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12150651.369793259, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12150651.369793259, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11964850.854771722, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11964850.854771722, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11792543.263225015, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11792543.263225015, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11631302.416094316, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11631302.416094316, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11479227.7561279, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11479227.7561279, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11334963.041737791, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11334963.041737791, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11197685.003164051, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11197685.003164051, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11067056.224580359, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11067056.224580359, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10943139.511030385, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10943139.511030385, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10826278.220752902, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10826278.220752902, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10716956.341549171, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10716956.341549171, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10615659.18706467, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10615659.18706467, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10522756.819315987, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10522756.819315987, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10438426.844454892, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10438426.844454892, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10362623.27115231, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10362623.27115231, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10295087.38179537, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10295087.38179537, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10235388.466414705, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10235388.466414705, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10182978.74114095, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10182978.74114095, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10137247.95260475, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10137247.95260475, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10097567.748922419, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10097567.748922419, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10063321.789749103, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10063321.789749103, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10033922.656392608, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10033922.656392608, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10008819.486834103, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10008819.486834103, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9987500.645290056, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9987500.645290056, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9969494.453323793, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9969494.453323793, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9954369.32479691, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9954369.32479691, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9941733.515465437, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9941733.515465437, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9931234.335989065, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9931234.335989065, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9922556.777457738, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9922556.777457738, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9915421.679110043, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9915421.679110043, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9909583.627876062, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9909583.627876062, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904828.718921196, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904828.718921196, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9900972.216495434, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9900972.216495434, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9897856.106707212, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9897856.106707212, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9895346.540855931, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9895346.540855931, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9893331.203755055, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9893331.203755055, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9891716.674948297, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9891716.674948297, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9890425.865192825, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9890425.865192825, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9889395.604661888, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9889395.604661888, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9888574.440116646, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9888574.440116646, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887920.674489552, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887920.674489552, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887400.660169175, tolerance: 4466.452064951529\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887400.660169175, tolerance: 4466.452064951529\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225193.80408011, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225193.80408011, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225110.813517075, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225110.813517075, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225006.093373984, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225006.093373984, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224873.954836704, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224873.954836704, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224707.22016197, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224707.22016197, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224496.83322094, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224496.83322094, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224231.36831536, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224231.36831536, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223896.410779048, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223896.410779048, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223473.77603032, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223473.77603032, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222940.525154293, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222940.525154293, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222267.72434169, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222267.72434169, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22221418.88207672, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22221418.88207672, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22220347.981225494, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22220347.981225494, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22218997.002387535, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22218997.002387535, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22217292.809172478, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22217292.809172478, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22215143.234477364, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22215143.234477364, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22212432.168317866, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22212432.168317866, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22209013.40126823, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22209013.40126823, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22204702.922219783, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22204702.922219783, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22199269.304569546, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22199269.304569546, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22192421.741654057, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22192421.741654057, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22183795.21258787, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22183795.21258787, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22172932.17909693, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22172932.17909693, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159260.14304964, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159260.14304964, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22142064.35203175, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22142064.35203175, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22120454.95809202, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22120454.95809202, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22093328.06334204, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22093328.06334204, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22059320.403233726, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22059320.403233726, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22016758.03845356, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22016758.03845356, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21963600.508906867, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21963600.508906867, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21897383.65478575, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21897383.65478575, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21815166.968429696, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21815166.968429696, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21713495.123522323, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21713495.123522323, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21588388.30984886, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21588388.30984886, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21435381.888817236, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21435381.888817236, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21249641.65996918, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21249641.65996918, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21026184.505123127, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21026184.505123127, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20760231.655652393, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20760231.655652393, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20447708.31167379, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20447708.31167379, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20085874.018901825, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20085874.018901825, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19674021.850113407, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19674021.850113407, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19214128.3053442, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19214128.3053442, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18711289.424232315, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18711289.424232315, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18173771.014405873, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18173771.014405873, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17612557.62934444, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17612557.62934444, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17040407.03219554, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17040407.03219554, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16470567.131662391, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16470567.131662391, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15915425.819018744, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15915425.819018744, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15385384.27148134, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15385384.27148134, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14888160.528800251, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14888160.528800251, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14428585.410549453, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14428585.410549453, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14008814.608291918, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14008814.608291918, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13628800.43657365, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13628800.43657365, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13286857.361730725, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13286857.361730725, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12980197.224087035, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12980197.224087035, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12705370.410345197, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12705370.410345197, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12458600.903357476, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12458600.903357476, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12236032.77957509, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12236032.77957509, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12033913.49389702, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12033913.49389702, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11848733.596731743, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11848733.596731743, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11677333.403468838, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11677333.403468838, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11516981.070353946, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11516981.070353946, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11365424.704670552, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11365424.704670552, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11220921.203073826, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11220921.203073826, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11082243.920528807, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11082243.920528807, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10948669.457422748, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10948669.457422748, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10819942.016223667, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10819942.016223667, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10696213.317397818, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10696213.317397818, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10577957.546131799, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10577957.546131799, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10465864.125929628, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10465864.125929628, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10360715.842557454, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10360715.842557454, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10263264.873279892, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10263264.873279892, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10174122.558233691, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10174122.558233691, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10093678.084935276, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10093678.084935276, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10022055.90958463, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10022055.90958463, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9959113.332252601, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9959113.332252601, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904471.381944738, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904471.381944738, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9857566.988895562, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9857566.988895562, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9817713.479318874, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9817713.479318874, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9784158.875562938, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9784158.875562938, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9756135.4293958, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9756135.4293958, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9732897.547924208, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9732897.547924208, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9713747.933154197, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9713747.933154197, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9698053.28484727, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9698053.28484727, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9685251.610393653, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9685251.610393653, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9674853.346299471, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9674853.346299471, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9666438.328081315, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9666438.328081315, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9659650.291029936, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9659650.291029936, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654190.159247063, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654190.159247063, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9649808.977198932, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9649808.977198932, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9646301.012972398, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9646301.012972398, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9643497.331329834, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9643497.331329834, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9641259.984843817, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9641259.984843817, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9639476.879484767, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9639476.879484767, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9638057.315691978, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9638057.315691978, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636928.172691077, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636928.172691077, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636030.684258943, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636030.684258943, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9635317.743812287, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9635317.743812287, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634751.672914779, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634751.672914779, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634302.388158696, tolerance: 4445.102149685068\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634302.388158696, tolerance: 4445.102149685068\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182535.705905367, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182535.705905367, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182443.31748153, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182443.31748153, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182326.738805104, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182326.738805104, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182179.636849403, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182179.636849403, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181994.021044992, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181994.021044992, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181759.809716668, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181759.809716668, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181464.28327085, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181464.28327085, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181091.39464482, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181091.39464482, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180620.89990636, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180620.89990636, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180027.262331244, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180027.262331244, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22179278.27131426, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22179278.27131426, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22178333.30250969, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22178333.30250969, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22177141.126954924, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22177141.126954924, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22175637.153777506, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22175637.153777506, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22173739.962460298, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22173739.962460298, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22171346.945451487, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22171346.945451487, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22168328.83898362, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22168328.83898362, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22164522.86814139, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22164522.86814139, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159724.170510717, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159724.170510717, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22153675.090679448, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22153675.090679448, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22146051.856030278, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22146051.856030278, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22136448.05522982, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22136448.05522982, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22124354.250566233, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22124354.250566233, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22109132.97552027, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22109132.97552027, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22089988.320511386, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22089988.320511386, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22065929.327634364, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22065929.327634364, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22035726.555516478, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22035726.555516478, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21997861.52451259, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21997861.52451259, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21950469.43740475, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21950469.43740475, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21891276.769023754, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21891276.769023754, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21817537.260214396, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21817537.260214396, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21725972.80421009, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21725972.80421009, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21612729.92224466, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21612729.92224466, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21473368.08108258, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21473368.08108258, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21302902.69437747, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21302902.69437747, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21095932.158423126, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21095932.158423126, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20846882.286273133, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20846882.286273133, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20550398.911674757, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20550398.911674757, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201904.639180813, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201904.639180813, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19798303.254432607, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19798303.254432607, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19338763.60317261, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19338763.60317261, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18825451.629099563, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18825451.629099563, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18264026.303933263, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18264026.303933263, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17663705.112665202, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17663705.112665202, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17036766.85903686, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17036766.85903686, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16397496.223623449, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16397496.223623449, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15760744.92149185, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15760744.92149185, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140415.226936534, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140415.226936534, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14548197.66197113, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14548197.66197113, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13992801.316187331, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13992801.316187331, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13479749.374918321, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13479749.374918321, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13011650.716625076, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13011650.716625076, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12588761.536151327, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12588761.536151327, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12209637.009462353, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12209637.009462353, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11871722.016013274, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11871722.016013274, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11571805.12738007, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11571805.12738007, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11306328.206388844, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11306328.206388844, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11071585.798533637, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11071585.798533637, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10863860.419768564, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10863860.419768564, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10679529.96999051, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10679529.96999051, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10515164.967978276, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10515164.967978276, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10367617.395431116, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10367617.395431116, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10234094.932508666, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10234094.932508666, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10112213.557229094, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10112213.557229094, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10000024.23575536, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10000024.23575536, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9896012.57105465, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9896012.57105465, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9799072.614562154, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9799072.614562154, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9708457.890308099, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9708457.890308099, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9623714.619163413, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9623714.619163413, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9544604.25348744, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9544604.25348744, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471024.212762302, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471024.212762302, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9402936.228999598, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9402936.228999598, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9340310.144654753, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9340310.144654753, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9283087.29859646, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9283087.29859646, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9231162.854348717, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9231162.854348717, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9184382.359520858, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9184382.359520858, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9142546.024753645, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9142546.024753645, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9105415.114890352, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9105415.114890352, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9072717.557093456, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9072717.557093456, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9044152.703403218, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9044152.703403218, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9019396.685310023, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9019396.685310023, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8998109.575324507, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8998109.575324507, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8979944.333787149, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8979944.333787149, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8964556.387394711, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8964556.387394711, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8951612.3695335, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8951612.3695335, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8940797.002727017, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8940797.002727017, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8931817.822045382, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8931817.822045382, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8924407.976697778, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8924407.976697778, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8918327.548498938, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8918327.548498938, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8913363.779400796, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8913363.779400796, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8909330.473245148, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8909330.473245148, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8906066.743651448, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8906066.743651448, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8903435.248435475, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8903435.248435475, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8901320.0564094, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8901320.0564094, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8899624.301448012, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8899624.301448012, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8898267.772619218, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8898267.772619218, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8897184.565959448, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8897184.565959448, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8896320.890152896, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8896320.890152896, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895633.08348321, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895633.08348321, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895085.869018715, tolerance: 4436.577708196869\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895085.869018715, tolerance: 4436.577708196869\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -6863,10 +6863,10 @@ "id": "3683157f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.015165Z", - "iopub.status.busy": "2023-08-21T02:29:31.015003Z", - "iopub.status.idle": "2023-08-21T02:29:31.140464Z", - "shell.execute_reply": "2023-08-21T02:29:31.139654Z" + "iopub.execute_input": "2023-08-22T07:00:14.307687Z", + "iopub.status.busy": "2023-08-22T07:00:14.307502Z", + "iopub.status.idle": "2023-08-22T07:00:14.399739Z", + "shell.execute_reply": "2023-08-22T07:00:14.399295Z" } }, "outputs": [ @@ -6910,10 +6910,10 @@ "id": "1d504d1b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.144567Z", - "iopub.status.busy": "2023-08-21T02:29:31.144409Z", - "iopub.status.idle": "2023-08-21T02:29:31.154211Z", - "shell.execute_reply": "2023-08-21T02:29:31.151804Z" + "iopub.execute_input": "2023-08-22T07:00:14.401869Z", + "iopub.status.busy": "2023-08-22T07:00:14.401710Z", + "iopub.status.idle": "2023-08-22T07:00:14.404500Z", + "shell.execute_reply": "2023-08-22T07:00:14.404205Z" } }, "outputs": [ @@ -6949,10 +6949,10 @@ "id": "8503f6ed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.164012Z", - "iopub.status.busy": "2023-08-21T02:29:31.163779Z", - "iopub.status.idle": "2023-08-21T02:29:31.173705Z", - "shell.execute_reply": "2023-08-21T02:29:31.169330Z" + "iopub.execute_input": "2023-08-22T07:00:14.406204Z", + "iopub.status.busy": "2023-08-22T07:00:14.406092Z", + "iopub.status.idle": "2023-08-22T07:00:14.408483Z", + "shell.execute_reply": "2023-08-22T07:00:14.408172Z" }, "lines_to_next_cell": 0 }, @@ -7014,10 +7014,10 @@ "id": "47ab71ff", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.190830Z", - "iopub.status.busy": "2023-08-21T02:29:31.190341Z", - "iopub.status.idle": "2023-08-21T02:29:31.196927Z", - "shell.execute_reply": "2023-08-21T02:29:31.195551Z" + "iopub.execute_input": "2023-08-22T07:00:14.410278Z", + "iopub.status.busy": "2023-08-22T07:00:14.410146Z", + "iopub.status.idle": "2023-08-22T07:00:14.412596Z", + "shell.execute_reply": "2023-08-22T07:00:14.412250Z" } }, "outputs": [], @@ -7041,10 +7041,10 @@ "id": "825b7073", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.199687Z", - "iopub.status.busy": "2023-08-21T02:29:31.199561Z", - "iopub.status.idle": "2023-08-21T02:29:31.520903Z", - "shell.execute_reply": "2023-08-21T02:29:31.520496Z" + "iopub.execute_input": "2023-08-22T07:00:14.414295Z", + "iopub.status.busy": "2023-08-22T07:00:14.414146Z", + "iopub.status.idle": "2023-08-22T07:00:14.676526Z", + "shell.execute_reply": "2023-08-22T07:00:14.676174Z" }, "lines_to_next_cell": 0 }, @@ -7053,2007 +7053,2007 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002961.893047336, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002961.893047336, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002909.292721532, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002909.292721532, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002842.919898538, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002842.919898538, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002759.16890147, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002759.16890147, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002653.490324104, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002653.490324104, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002520.144170538, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002520.144170538, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002351.888507718, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002351.888507718, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002139.586836109, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002139.586836109, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001871.713040235, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001871.713040235, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001533.727331886, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001533.727331886, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001107.28977405, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001107.28977405, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000569.269442707, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000569.269442707, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999890.496647634, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999890.496647634, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999034.192416634, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999034.192416634, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997953.993094172, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997953.993094172, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996591.467783943, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996591.467783943, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994873.001788342, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994873.001788342, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15992705.889472542, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15992705.889472542, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15989973.444502639, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15989973.444502639, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15986528.893835295, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15986528.893835295, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15982187.774395373, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15982187.774395373, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976718.499356627, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976718.499356627, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15969830.707495732, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15969830.707495732, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15961160.960501963, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15961160.960501963, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15950255.320705947, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15950255.320705947, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15936548.344581451, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15936548.344581451, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15919338.096469924, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15919338.096469924, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15897756.97009871, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15897756.97009871, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15870738.473491088, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15870738.473491088, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15836980.785622943, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15836980.785622943, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15794908.961932577, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15794908.961932577, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15742639.305781398, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15742639.305781398, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15677951.783964379, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15677951.783964379, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15598279.520216344, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15598279.520216344, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15500728.213326858, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15500728.213326858, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15382142.225333132, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15382142.225333132, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15239236.776243072, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15239236.776243072, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068814.890988702, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068814.890988702, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14868080.263148528, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14868080.263148528, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14635039.685599191, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14635039.685599191, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14368959.698660212, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14368959.698660212, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14070805.23862632, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14070805.23862632, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13743554.88143778, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13743554.88143778, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13392276.560592549, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13392276.560592549, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13023877.88091306, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13023877.88091306, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12646520.933576018, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12646520.933576018, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12268792.343592053, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12268792.343592053, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11898803.095559342, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11898803.095559342, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11543417.93091813, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11543417.93091813, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11207766.718773343, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11207766.718773343, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10895093.611569963, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10895093.611569963, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10606899.312997252, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10606899.312997252, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10343266.88124088, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10343266.88124088, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10103247.353431445, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10103247.353431445, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9885208.910573516, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9885208.910573516, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9687100.478192497, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9687100.478192497, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9506625.781409387, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9506625.781409387, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9341352.903950285, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9341352.903950285, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9188793.402093235, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9188793.402093235, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9046478.453631114, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9046478.453631114, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8912045.904589174, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8912045.904589174, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8783339.107432563, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8783339.107432563, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8658509.901020331, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8658509.901020331, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8536113.828113679, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8536113.828113679, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8415183.975072118, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8415183.975072118, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8295269.742745581, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8295269.742745581, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8176429.120013418, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8176429.120013418, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8059168.8293056125, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8059168.8293056125, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7944335.999206969, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7944335.999206969, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7832975.645216511, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7832975.645216511, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7726176.614947152, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7726176.614947152, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7624931.461247044, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7624931.461247044, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7530031.627469164, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7530031.627469164, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7442009.746564693, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7442009.746564693, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7361129.1469736025, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7361129.1469736025, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7287410.63533624, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7287410.63533624, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7220681.095616933, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7220681.095616933, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7160628.395404535, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7160628.395404535, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7106851.48376607, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7106851.48376607, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7058900.76970095, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7058900.76970095, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7016308.880858365, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7016308.880858365, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6978613.911777701, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6978613.911777701, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945376.571027264, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945376.571027264, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6916191.049528801, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6916191.049528801, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6890688.79244657, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6890688.79244657, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6868535.393319955, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6868535.393319955, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6849422.765039895, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6849422.765039895, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6833060.05095333, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6833060.05095333, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819166.544534292, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819166.544534292, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6807468.458908728, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6807468.458908728, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797699.628345776, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797699.628345776, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6789604.944998693, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6789604.944998693, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6782944.868629447, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6782944.868629447, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6777499.565630652, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6777499.565630652, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6773071.791553852, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6773071.791553852, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6769488.209512218, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6769488.209512218, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6766599.256783063, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6766599.256783063, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6764277.892213011, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6764277.892213011, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6762417.6162482, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6762417.6162482, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6760930.116967933, tolerance: 3200.6325551004925\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6760930.116967933, tolerance: 3200.6325551004925\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173612.82487654, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173612.82487654, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173560.33151807, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173560.33151807, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173494.093703294, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173494.093703294, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173410.51311625, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173410.51311625, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173305.049649913, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173305.049649913, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173171.975059805, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173171.975059805, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173004.062268812, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173004.062268812, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172792.193566969, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172792.193566969, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172524.866617758, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172524.866617758, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172187.571748763, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172187.571748763, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171762.00720005, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171762.00720005, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171225.090500388, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171225.090500388, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15170547.71354342, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15170547.71354342, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15169693.175771877, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15169693.175771877, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15168615.213598879, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15168615.213598879, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15167255.524179863, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15167255.524179863, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15165540.657224856, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15165540.657224856, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15163378.11903821, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15163378.11903821, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15160651.497821936, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15160651.497821936, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15157214.378191706, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15157214.378191706, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15152882.766135195, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15152882.766135195, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15147425.6946986, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15147425.6946986, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140553.628850497, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140553.628850497, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15131904.241777299, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15131904.241777299, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15121025.105980713, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15121025.105980713, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15107352.850599289, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15107352.850599289, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15090188.41286841, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15090188.41286841, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068668.205066573, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068668.205066573, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15041731.400110113, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15041731.400110113, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15008084.208955988, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15008084.208955988, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14966163.110870235, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14966163.110870235, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14914100.653844737, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14914100.653844737, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14849699.805850953, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14849699.805850953, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14770425.961151276, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14770425.961151276, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14673429.41690654, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14673429.41690654, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14555614.815015966, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14555614.815015966, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14413776.349016687, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14413776.349016687, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14244816.178940995, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14244816.178940995, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14046055.366934752, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14046055.366934752, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13815628.708094303, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13815628.708094303, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13552926.205683708, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13552926.205683708, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13259008.940702371, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13259008.940702371, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12936897.573228309, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12936897.573228309, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12591625.616217315, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12591625.616217315, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12229982.920676824, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12229982.920676824, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11859948.802383406, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11859948.802383406, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11489906.8603167, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11489906.8603167, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11127805.377401602, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11127805.377401602, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10780443.14443526, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10780443.14443526, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10453012.587348029, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10453012.587348029, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10148944.578529166, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10148944.578529166, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9870012.667698365, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9870012.667698365, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9616601.230672905, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9616601.230672905, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9388032.941233683, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9388032.941233683, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9182876.289070565, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9182876.289070565, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8999193.791535858, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8999193.791535858, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8834727.19434187, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8834727.19434187, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8687036.347689679, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8687036.347689679, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553612.383287674, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553612.383287674, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8431979.280234471, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8431979.280234471, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8319788.946660187, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8319788.946660187, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8214909.054690647, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8214909.054690647, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8115501.1056430675, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8115501.1056430675, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8020086.35524954, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8020086.35524954, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7927596.53846852, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7927596.53846852, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7837403.822275469, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7837403.822275469, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7749321.535335509, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7749321.535335509, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7663566.802084766, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7663566.802084766, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7580680.550684168, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7580680.550684168, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7501409.564666606, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7501409.564666606, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7426566.500521793, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7426566.500521793, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7356892.242162683, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7356892.242162683, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7292946.117484922, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7292946.117484922, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7235042.041596897, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7235042.041596897, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7183235.551674365, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7183235.551674365, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7137353.553695727, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7137353.553695727, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097050.348456673, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097050.348456673, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061872.012726546, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061872.012726546, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7031315.123405474, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7031315.123405474, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7004872.089238734, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7004872.089238734, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6982061.123036072, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6982061.123036072, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6962442.578610088, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6962442.578610088, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945624.890073966, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945624.890073966, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6931263.4663270125, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6931263.4663270125, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6919055.476661856, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6919055.476661856, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6908732.977539104, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6908732.977539104, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6900056.2920428645, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6900056.2920428645, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6892808.858171555, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6892808.858171555, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886793.977603645, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886793.977603645, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6881833.233569127, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6881833.233569127, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6877765.97498893, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6877765.97498893, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6874449.207170451, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6874449.207170451, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6871757.386867455, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6871757.386867455, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6869581.853912757, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6869581.853912757, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6867829.838852352, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6867829.838852352, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6866423.119345377, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6866423.119345377, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865296.456501478, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865296.456501478, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6864395.94700243, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6864395.94700243, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863677.402652601, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863677.402652601, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863104.834999971, tolerance: 3034.7626598069196\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863104.834999971, tolerance: 3034.7626598069196\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000126.775776321, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000126.775776321, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000067.997791689, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000067.997791689, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999993.829780785, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999993.829780785, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999900.242584623, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999900.242584623, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999782.152469946, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999782.152469946, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999633.14527111, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999633.14527111, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999445.128467944, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999445.128467944, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999207.892430544, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999207.892430544, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998908.557207122, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998908.557207122, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998530.875140417, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998530.875140417, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998054.351968959, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998054.351968959, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997453.139532348, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997453.139532348, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996694.641307216, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996694.641307216, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15995737.757220387, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15995737.757220387, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994530.675893765, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994530.675893765, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15993008.099962447, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15993008.099962447, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15991087.762599917, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15991087.762599917, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15988666.060097354, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15988666.060097354, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15985612.585588472, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15985612.585588472, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15981763.302383827, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15981763.302383827, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976912.042096594, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976912.042096594, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15970799.954194367, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15970799.954194367, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15963102.47325135, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15963102.47325135, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15953413.314912459, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15953413.314912459, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15941224.973906962, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15941224.973906962, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15925905.198558565, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15925905.198558565, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15906668.990428165, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15906668.990428165, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15882545.878220897, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15882545.878220897, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15852342.621036042, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15852342.621036042, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15814602.219371142, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15814602.219371142, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15767561.301116722, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15767561.301116722, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15709109.781098895, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15709109.781098895, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15636759.341258615, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15636759.341258615, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15547630.840385439, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15547630.840385439, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15438475.105455775, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15438475.105455775, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15305746.07465526, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15305746.07465526, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15145748.542592412, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15145748.542592412, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14954882.27386727, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14954882.27386727, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14729996.36384661, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14729996.36384661, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14468848.510940228, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14468848.510940228, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14170631.317143818, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14170631.317143818, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13836485.361873377, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13836485.361873377, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13469879.089990832, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13469879.089990832, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13076719.754361462, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13076719.754361462, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12665089.79937819, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12665089.79937819, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12244586.676668119, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12244586.676668119, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11825360.36369123, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11825360.36369123, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11417044.801169304, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11417044.801169304, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11027817.645702794, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11027817.645702794, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10663776.910200799, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10663776.910200799, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10328716.2675956, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10328716.2675956, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10024263.64783793, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10024263.64783793, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9750266.819731826, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9750266.819731826, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9505284.688773429, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9505284.688773429, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9287065.61072085, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9287065.61072085, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9092940.776433397, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9092940.776433397, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8920108.266351493, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8920108.266351493, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8765816.866835387, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8765816.866835387, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8627473.905487, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8627473.905487, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8502702.196109628, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8502702.196109628, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8389365.458262948, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8389365.458262948, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8285575.962199782, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8285575.962199782, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8189695.129107317, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8189695.129107317, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8100335.848355204, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8100335.848355204, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8016371.614804332, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8016371.614804332, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7936951.343980087, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7936951.343980087, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7861511.843847987, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7861511.843847987, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7789775.81877588, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7789775.81877588, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7721724.491724883, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7721724.491724883, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7657540.53569288, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7657540.53569288, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7597526.506006125, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7597526.506006125, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7542012.431576015, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7542012.431576015, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7491270.131106991, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7491270.131106991, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7445449.741931618, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7445449.741931618, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7404547.164364969, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7404547.164364969, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7368402.734578147, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7368402.734578147, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7336724.612267373, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7336724.612267373, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7309126.908337014, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7309126.908337014, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7285172.53934286, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7285172.53934286, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7264413.0266303, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7264413.0266303, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7246420.465473388, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7246420.465473388, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7230809.549602925, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7230809.549602925, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7217249.407961924, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7217249.407961924, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7205466.206412938, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7205466.206412938, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7195238.325930048, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7195238.325930048, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186386.647782039, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186386.647782039, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7178762.875061372, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7178762.875061372, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7172238.602284237, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7172238.602284237, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7166697.001612171, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7166697.001612171, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7162027.848205515, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7162027.848205515, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7158125.584421616, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7158125.584421616, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7154889.512672326, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7154889.512672326, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7152225.062096559, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7152225.062096559, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7150045.262096591, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7150045.262096591, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7148271.882784204, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7148271.882784204, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7146836.014641162, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7146836.014641162, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7145678.080780019, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7145678.080780019, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144747.393668608, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144747.393668608, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144001.407092072, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144001.407092072, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7143404.805305656, tolerance: 3200.070250165819\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7143404.805305656, tolerance: 3200.070250165819\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766426.844425442, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766426.844425442, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766379.012219734, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766379.012219734, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766318.655993313, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766318.655993313, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766242.496938994, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766242.496938994, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766146.398082258, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766146.398082258, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766025.13980752, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766025.13980752, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765872.136748439, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765872.136748439, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765679.08077331, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765679.08077331, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765435.490848666, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765435.490848666, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765128.145612368, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765128.145612368, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764740.368286435, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764740.368286435, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764251.12581003, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764251.12581003, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13763633.894413952, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13763633.894413952, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13762855.231859002, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13762855.231859002, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13761872.98172164, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13761872.98172164, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13760634.01686267, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13760634.01686267, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13759071.406945651, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13759071.406945651, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13757100.867966294, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13757100.867966294, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13754616.31968939, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13754616.31968939, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13751484.339396805, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13751484.339396805, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13747537.257695232, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13747537.257695232, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13742564.595583746, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13742564.595583746, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13736302.49455343, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13736302.49455343, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13728420.749109622, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13728420.749109622, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13718507.02436845, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13718507.02436845, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13706047.848124275, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13706047.848124275, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13690406.03569032, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13690406.03569032, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13670794.381086988, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13670794.381086988, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13646245.795015218, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13646245.795015218, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13615580.679837886, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13615580.679837886, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13577373.323622873, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13577373.323622873, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13529920.608156208, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13529920.608156208, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13471218.48980598, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13471218.48980598, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13398954.581488008, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13398954.581488008, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13310528.590455977, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13310528.590455977, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13203115.797389356, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13203115.797389356, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13073790.981404455, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13073790.981404455, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12919729.112886174, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12919729.112886174, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12738491.873820404, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12738491.873820404, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12528392.752768412, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12528392.752768412, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12288907.120278118, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12288907.120278118, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12021061.050642934, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12021061.050642934, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11727704.457379244, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11727704.457379244, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11413566.98420337, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11413566.98420337, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11085024.381464427, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11085024.381464427, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10749570.986969216, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10749570.986969216, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10415080.823900381, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10415080.823900381, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089009.138994666, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089009.138994666, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9777704.218602655, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9777704.218602655, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9485957.157639354, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9485957.157639354, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9216836.907742979, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9216836.907742979, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8971777.239570614, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8971777.239570614, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8750831.806329573, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8750831.806329573, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553002.845594905, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553002.845594905, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8376568.552591967, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8376568.552591967, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8219365.99395707, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8219365.99395707, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8079015.288983279, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8079015.288983279, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7953088.9445123505, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7953088.9445123505, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7839237.297915861, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7839237.297915861, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7735280.8174845725, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7735280.8174845725, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7639277.0523840925, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7639277.0523840925, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7549567.214150196, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7549567.214150196, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7464805.436922483, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7464805.436922483, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7383972.203368484, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7383972.203368484, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7306371.938584399, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7306371.938584399, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7231613.9732326735, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7231613.9732326735, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7159576.877369866, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7159576.877369866, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7090358.567763316, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7090358.567763316, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7024217.2241215855, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7024217.2241215855, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6961509.172985473, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6961509.172985473, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6902628.941757254, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6902628.941757254, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6847954.742128507, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6847954.742128507, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797801.388530005, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797801.388530005, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6752382.798167879, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6752382.798167879, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6711786.944628094, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6711786.944628094, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6675965.981966857, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6675965.981966857, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6644742.448857503, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6644742.448857503, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6617829.550839442, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6617829.550839442, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6594860.867273544, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6594860.867273544, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6575423.588385814, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6575423.588385814, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6559089.833983606, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6559089.833983606, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6545442.225937976, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6545442.225937976, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6534091.895329608, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6534091.895329608, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6524688.873515937, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6524688.873515937, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6516926.039701696, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6516926.039701696, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6510538.426567688, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6510538.426567688, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6505299.7780512385, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6505299.7780512385, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6501017.943079299, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6501017.943079299, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6497530.176477653, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6497530.176477653, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6494698.902794392, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6494698.902794392, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6492408.111473216, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6492408.111473216, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6490560.3336993465, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6490560.3336993465, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6489074.074242126, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6489074.074242126, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6487881.578697735, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6487881.578697735, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486926.855244275, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486926.855244275, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486163.908028902, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486163.908028902, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485555.163897053, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485555.163897053, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485070.084972435, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485070.084972435, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484683.961142942, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484683.961142942, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484376.8736711275, tolerance: 2753.321903486231\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484376.8736711275, tolerance: 2753.321903486231\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123836.286658319, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123836.286658319, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123762.414447501, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123762.414447501, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123669.200043006, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123669.200043006, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123551.579596577, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123551.579596577, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123403.163871313, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123403.163871313, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123215.891543608, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123215.891543608, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122979.591935372, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122979.591935372, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122681.433587788, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122681.433587788, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122305.228986472, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122305.228986472, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121830.55809336, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121830.55809336, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121231.663752725, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121231.663752725, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16120476.060052717, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16120476.060052717, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16119522.779778486, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16119522.779778486, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16118320.168518286, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16118320.168518286, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16116803.109996723, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16116803.109996723, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16114889.538918179, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16114889.538918179, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16112476.063036688, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16112476.063036688, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16109432.47434148, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16109432.47434148, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16105594.879294181, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16105594.879294181, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16100757.119470121, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16100757.119470121, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16094660.087017829, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16094660.087017829, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16086978.46580684, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16086978.46580684, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16077304.35332688, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16077304.35332688, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16065127.149018394, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16065127.149018394, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16049809.047450969, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16049809.047450969, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16030555.476241706, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16030555.476241706, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16006379.911872495, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16006379.911872495, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976062.758394275, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976062.758394275, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15938104.483596483, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15938104.483596483, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15890674.11469827, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15890674.11469827, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15831555.686060235, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15831555.686060235, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15758097.525340755, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15758097.525340755, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15667172.578206709, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15667172.578206709, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15555162.420748936, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15555162.420748936, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15417983.020182043, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15417983.020182043, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15251175.908593165, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15251175.908593165, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15050092.453317674, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15050092.453317674, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14810198.177746587, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14810198.177746587, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14527514.082835246, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14527514.082835246, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14199187.811678281, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14199187.811678281, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13824146.920817537, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13824146.920817537, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13403734.027286602, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13403734.027286602, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12942174.869677957, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12942174.869677957, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12446711.659031235, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12446711.659031235, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11927272.408043081, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11927272.408043081, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11395650.820912804, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11395650.820912804, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864314.587176824, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864314.587176824, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10345084.699656613, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10345084.699656613, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9847974.664610261, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9847974.664610261, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9380422.144947704, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9380422.144947704, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8947015.008946363, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8947015.008946363, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8549670.25861056, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8549670.25861056, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188124.101396974, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188124.101396974, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7860558.677097185, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7860558.677097185, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7564216.251072414, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7564216.251072414, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295907.831051512, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295907.831051512, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7052382.339382325, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7052382.339382325, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6830565.9531656, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6830565.9531656, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6627701.871803495, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6627701.871803495, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6441421.548990706, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6441421.548990706, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6269768.629955583, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6269768.629955583, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6111186.722532658, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6111186.722532658, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5964477.8422521455, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5964477.8422521455, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5828739.876908956, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5828739.876908956, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5703294.550898104, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5703294.550898104, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5587617.998651163, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5587617.998651163, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5481282.987854576, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5481282.987854576, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5383916.678079197, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5383916.678079197, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5295172.882818847, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5295172.882818847, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5214714.536832884, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5214714.536832884, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5142200.898831903, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5142200.898831903, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5077274.992035702, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5077274.992035702, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5019549.576235791, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5019549.576235791, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4968593.444998971, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4968593.444998971, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4923922.319001433, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4923922.319001433, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4884998.717484867, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4884998.717484867, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4851242.93844572, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4851242.93844572, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4822053.96370539, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4822053.96370539, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4796836.339338534, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4796836.339338534, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4775027.808895556, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4775027.808895556, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4756122.72319144, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4756122.72319144, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4739687.533593078, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4739687.533593078, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4725366.495343714, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4725366.495343714, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4712877.711579567, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4712877.711579567, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4702001.540622955, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4702001.540622955, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4692564.7733192, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4692564.7733192, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4684424.413915036, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4684424.413915036, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4677454.213645212, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4677454.213645212, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4671535.662147184, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4671535.662147184, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4666553.581406483, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4666553.581406483, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4662395.34181063, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4662395.34181063, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4658952.2530382, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4658952.2530382, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4656121.7760819215, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4656121.7760819215, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4653809.600037627, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4653809.600037627, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4651931.081491712, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4651931.081491712, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4650411.90595999, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4650411.90595999, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4649188.052146216, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4649188.052146216, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4648205.23751574, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4648205.23751574, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4647418.038791819, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4647418.038791819, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4646788.852992944, tolerance: 3224.823681413525\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:614: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4646788.852992944, tolerance: 3224.823681413525\n", " model = cd_fast.enet_coordinate_descent_gram(\n", - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 3.855e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 3.855e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", " model = cd_fast.enet_coordinate_descent(\n" ] }, @@ -9107,10 +9107,10 @@ "id": "efb2d25e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.523410Z", - "iopub.status.busy": "2023-08-21T02:29:31.523272Z", - "iopub.status.idle": "2023-08-21T02:29:31.579951Z", - "shell.execute_reply": "2023-08-21T02:29:31.579678Z" + "iopub.execute_input": "2023-08-22T07:00:14.678953Z", + "iopub.status.busy": "2023-08-22T07:00:14.678802Z", + "iopub.status.idle": "2023-08-22T07:00:14.728316Z", + "shell.execute_reply": "2023-08-22T07:00:14.727946Z" }, "lines_to_next_cell": 2 }, @@ -9143,10 +9143,10 @@ "id": "5d2977a4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.581483Z", - "iopub.status.busy": "2023-08-21T02:29:31.581387Z", - "iopub.status.idle": "2023-08-21T02:29:31.592388Z", - "shell.execute_reply": "2023-08-21T02:29:31.592127Z" + "iopub.execute_input": "2023-08-22T07:00:14.730133Z", + "iopub.status.busy": "2023-08-22T07:00:14.730000Z", + "iopub.status.idle": "2023-08-22T07:00:14.740248Z", + "shell.execute_reply": "2023-08-22T07:00:14.739889Z" }, "lines_to_next_cell": 0 }, @@ -9177,10 +9177,10 @@ "id": "0d833201", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.593898Z", - "iopub.status.busy": "2023-08-21T02:29:31.593816Z", - "iopub.status.idle": "2023-08-21T02:29:31.766535Z", - "shell.execute_reply": "2023-08-21T02:29:31.766109Z" + "iopub.execute_input": "2023-08-22T07:00:14.742261Z", + "iopub.status.busy": "2023-08-22T07:00:14.742118Z", + "iopub.status.idle": "2023-08-22T07:00:14.896514Z", + "shell.execute_reply": "2023-08-22T07:00:14.896137Z" }, "lines_to_next_cell": 0 }, @@ -9220,10 +9220,10 @@ "id": "bfbf2fe3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.768327Z", - "iopub.status.busy": "2023-08-21T02:29:31.768191Z", - "iopub.status.idle": "2023-08-21T02:29:31.770716Z", - "shell.execute_reply": "2023-08-21T02:29:31.770428Z" + "iopub.execute_input": "2023-08-22T07:00:14.898477Z", + "iopub.status.busy": "2023-08-22T07:00:14.898358Z", + "iopub.status.idle": "2023-08-22T07:00:14.900774Z", + "shell.execute_reply": "2023-08-22T07:00:14.900366Z" } }, "outputs": [ @@ -9256,10 +9256,10 @@ "id": "ccb9a209", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.772272Z", - "iopub.status.busy": "2023-08-21T02:29:31.772160Z", - "iopub.status.idle": "2023-08-21T02:29:31.881691Z", - "shell.execute_reply": "2023-08-21T02:29:31.881374Z" + "iopub.execute_input": "2023-08-22T07:00:14.902539Z", + "iopub.status.busy": "2023-08-22T07:00:14.902374Z", + "iopub.status.idle": "2023-08-22T07:00:14.997062Z", + "shell.execute_reply": "2023-08-22T07:00:14.996746Z" } }, "outputs": [ @@ -9303,10 +9303,10 @@ "id": "40eff15b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.883735Z", - "iopub.status.busy": "2023-08-21T02:29:31.883609Z", - "iopub.status.idle": "2023-08-21T02:29:31.886519Z", - "shell.execute_reply": "2023-08-21T02:29:31.886178Z" + "iopub.execute_input": "2023-08-22T07:00:14.998878Z", + "iopub.status.busy": "2023-08-22T07:00:14.998760Z", + "iopub.status.idle": "2023-08-22T07:00:15.001284Z", + "shell.execute_reply": "2023-08-22T07:00:15.000943Z" } }, "outputs": [ @@ -9373,10 +9373,10 @@ "id": "2bd0cc9c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.888390Z", - "iopub.status.busy": "2023-08-21T02:29:31.888274Z", - "iopub.status.idle": "2023-08-21T02:29:31.893813Z", - "shell.execute_reply": "2023-08-21T02:29:31.893422Z" + "iopub.execute_input": "2023-08-22T07:00:15.003376Z", + "iopub.status.busy": "2023-08-22T07:00:15.003216Z", + "iopub.status.idle": "2023-08-22T07:00:15.007328Z", + "shell.execute_reply": "2023-08-22T07:00:15.007008Z" } }, "outputs": [ @@ -9417,10 +9417,10 @@ "id": "f188c54a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.895683Z", - "iopub.status.busy": "2023-08-21T02:29:31.895544Z", - "iopub.status.idle": "2023-08-21T02:29:31.900117Z", - "shell.execute_reply": "2023-08-21T02:29:31.899738Z" + "iopub.execute_input": "2023-08-22T07:00:15.008975Z", + "iopub.status.busy": "2023-08-22T07:00:15.008880Z", + "iopub.status.idle": "2023-08-22T07:00:15.012449Z", + "shell.execute_reply": "2023-08-22T07:00:15.012179Z" } }, "outputs": [ @@ -9460,10 +9460,10 @@ "id": "8be53659", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:31.902369Z", - "iopub.status.busy": "2023-08-21T02:29:31.902221Z", - "iopub.status.idle": "2023-08-21T02:29:32.087080Z", - "shell.execute_reply": "2023-08-21T02:29:32.086738Z" + "iopub.execute_input": "2023-08-22T07:00:15.014465Z", + "iopub.status.busy": "2023-08-22T07:00:15.014315Z", + "iopub.status.idle": "2023-08-22T07:00:15.127942Z", + "shell.execute_reply": "2023-08-22T07:00:15.127535Z" } }, "outputs": [ @@ -9520,10 +9520,10 @@ "id": "b6c30e1c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:32.088803Z", - "iopub.status.busy": "2023-08-21T02:29:32.088665Z", - "iopub.status.idle": "2023-08-21T02:29:32.188991Z", - "shell.execute_reply": "2023-08-21T02:29:32.188578Z" + "iopub.execute_input": "2023-08-22T07:00:15.129800Z", + "iopub.status.busy": "2023-08-22T07:00:15.129659Z", + "iopub.status.idle": "2023-08-22T07:00:15.218853Z", + "shell.execute_reply": "2023-08-22T07:00:15.218439Z" } }, "outputs": [ @@ -9575,10 +9575,10 @@ "id": "6fedf71f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:32.190664Z", - "iopub.status.busy": "2023-08-21T02:29:32.190543Z", - "iopub.status.idle": "2023-08-21T02:29:32.196709Z", - "shell.execute_reply": "2023-08-21T02:29:32.196462Z" + "iopub.execute_input": "2023-08-22T07:00:15.220829Z", + "iopub.status.busy": "2023-08-22T07:00:15.220699Z", + "iopub.status.idle": "2023-08-22T07:00:15.226460Z", + "shell.execute_reply": "2023-08-22T07:00:15.226111Z" }, "lines_to_next_cell": 2 }, @@ -9621,10 +9621,10 @@ "id": "f78e9153", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:32.198160Z", - "iopub.status.busy": "2023-08-21T02:29:32.198072Z", - "iopub.status.idle": "2023-08-21T02:29:32.201083Z", - "shell.execute_reply": "2023-08-21T02:29:32.200686Z" + "iopub.execute_input": "2023-08-22T07:00:15.228509Z", + "iopub.status.busy": "2023-08-22T07:00:15.228362Z", + "iopub.status.idle": "2023-08-22T07:00:15.230933Z", + "shell.execute_reply": "2023-08-22T07:00:15.230633Z" } }, "outputs": [ @@ -9676,10 +9676,10 @@ "id": "31120c88", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:32.203509Z", - "iopub.status.busy": "2023-08-21T02:29:32.203359Z", - "iopub.status.idle": "2023-08-21T02:29:32.207977Z", - "shell.execute_reply": "2023-08-21T02:29:32.207598Z" + "iopub.execute_input": "2023-08-22T07:00:15.232659Z", + "iopub.status.busy": "2023-08-22T07:00:15.232549Z", + "iopub.status.idle": "2023-08-22T07:00:15.235970Z", + "shell.execute_reply": "2023-08-22T07:00:15.235650Z" } }, "outputs": [ @@ -9718,10 +9718,10 @@ "id": "fc6b3a12", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:32.210299Z", - "iopub.status.busy": "2023-08-21T02:29:32.210090Z", - "iopub.status.idle": "2023-08-21T02:29:32.358906Z", - "shell.execute_reply": "2023-08-21T02:29:32.358585Z" + "iopub.execute_input": "2023-08-22T07:00:15.237863Z", + "iopub.status.busy": "2023-08-22T07:00:15.237759Z", + "iopub.status.idle": "2023-08-22T07:00:15.333920Z", + "shell.execute_reply": "2023-08-22T07:00:15.333623Z" } }, "outputs": [ @@ -9771,10 +9771,10 @@ "id": "b806f0e8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:32.362552Z", - "iopub.status.busy": "2023-08-21T02:29:32.362360Z", - "iopub.status.idle": "2023-08-21T02:29:32.464295Z", - "shell.execute_reply": "2023-08-21T02:29:32.463871Z" + "iopub.execute_input": "2023-08-22T07:00:15.335731Z", + "iopub.status.busy": "2023-08-22T07:00:15.335610Z", + "iopub.status.idle": "2023-08-22T07:00:15.425776Z", + "shell.execute_reply": "2023-08-22T07:00:15.425408Z" } }, "outputs": [ @@ -9815,7 +9815,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -9828,7 +9828,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch07-nonlin-lab.Rmd b/Ch07-nonlin-lab.Rmd index c2abf72..c595752 100644 --- a/Ch07-nonlin-lab.Rmd +++ b/Ch07-nonlin-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch07-nonlin-lab.ipynb b/Ch07-nonlin-lab.ipynb index b56a2db..347036d 100644 --- a/Ch07-nonlin-lab.ipynb +++ b/Ch07-nonlin-lab.ipynb @@ -21,10 +21,10 @@ "id": "f0d9a8c8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:33.891853Z", - "iopub.status.busy": "2023-08-21T02:29:33.891740Z", - "iopub.status.idle": "2023-08-21T02:29:35.316371Z", - "shell.execute_reply": "2023-08-21T02:29:35.315923Z" + "iopub.execute_input": "2023-08-22T07:00:19.101721Z", + "iopub.status.busy": "2023-08-22T07:00:19.101624Z", + "iopub.status.idle": "2023-08-22T07:00:20.128515Z", + "shell.execute_reply": "2023-08-22T07:00:20.128166Z" } }, "outputs": [], @@ -55,10 +55,10 @@ "id": "c4bc71b4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.320427Z", - "iopub.status.busy": "2023-08-21T02:29:35.319735Z", - "iopub.status.idle": "2023-08-21T02:29:35.335825Z", - "shell.execute_reply": "2023-08-21T02:29:35.335435Z" + "iopub.execute_input": "2023-08-22T07:00:20.130689Z", + "iopub.status.busy": "2023-08-22T07:00:20.130382Z", + "iopub.status.idle": "2023-08-22T07:00:20.144261Z", + "shell.execute_reply": "2023-08-22T07:00:20.143915Z" } }, "outputs": [], @@ -94,10 +94,10 @@ "id": "14649fd7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.338249Z", - "iopub.status.busy": "2023-08-21T02:29:35.338114Z", - "iopub.status.idle": "2023-08-21T02:29:35.348243Z", - "shell.execute_reply": "2023-08-21T02:29:35.347922Z" + "iopub.execute_input": "2023-08-22T07:00:20.146307Z", + "iopub.status.busy": "2023-08-22T07:00:20.146145Z", + "iopub.status.idle": "2023-08-22T07:00:20.154062Z", + "shell.execute_reply": "2023-08-22T07:00:20.153676Z" } }, "outputs": [], @@ -124,10 +124,10 @@ "id": "bca84aa3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.350246Z", - "iopub.status.busy": "2023-08-21T02:29:35.350053Z", - "iopub.status.idle": "2023-08-21T02:29:35.474010Z", - "shell.execute_reply": "2023-08-21T02:29:35.473145Z" + "iopub.execute_input": "2023-08-22T07:00:20.155897Z", + "iopub.status.busy": "2023-08-22T07:00:20.155777Z", + "iopub.status.idle": "2023-08-22T07:00:20.258666Z", + "shell.execute_reply": "2023-08-22T07:00:20.253837Z" }, "lines_to_next_cell": 2 }, @@ -259,10 +259,10 @@ "id": "411af5ab", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.479757Z", - "iopub.status.busy": "2023-08-21T02:29:35.479078Z", - "iopub.status.idle": "2023-08-21T02:29:35.483997Z", - "shell.execute_reply": "2023-08-21T02:29:35.483275Z" + "iopub.execute_input": "2023-08-22T07:00:20.267608Z", + "iopub.status.busy": "2023-08-22T07:00:20.265087Z", + "iopub.status.idle": "2023-08-22T07:00:20.272250Z", + "shell.execute_reply": "2023-08-22T07:00:20.271081Z" } }, "outputs": [], @@ -294,10 +294,10 @@ "id": "0eb6317c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.487461Z", - "iopub.status.busy": "2023-08-21T02:29:35.487287Z", - "iopub.status.idle": "2023-08-21T02:29:35.493595Z", - "shell.execute_reply": "2023-08-21T02:29:35.491379Z" + "iopub.execute_input": "2023-08-22T07:00:20.278663Z", + "iopub.status.busy": "2023-08-22T07:00:20.278281Z", + "iopub.status.idle": "2023-08-22T07:00:20.286475Z", + "shell.execute_reply": "2023-08-22T07:00:20.285277Z" }, "lines_to_next_cell": 0 }, @@ -351,10 +351,10 @@ "id": "714f2c6d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.499091Z", - "iopub.status.busy": "2023-08-21T02:29:35.498388Z", - "iopub.status.idle": "2023-08-21T02:29:35.798204Z", - "shell.execute_reply": "2023-08-21T02:29:35.797853Z" + "iopub.execute_input": "2023-08-22T07:00:20.291120Z", + "iopub.status.busy": "2023-08-22T07:00:20.289650Z", + "iopub.status.idle": "2023-08-22T07:00:20.490780Z", + "shell.execute_reply": "2023-08-22T07:00:20.489475Z" }, "lines_to_next_cell": 0 }, @@ -420,10 +420,10 @@ "id": "0f5f60ed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.799903Z", - "iopub.status.busy": "2023-08-21T02:29:35.799807Z", - "iopub.status.idle": "2023-08-21T02:29:35.885877Z", - "shell.execute_reply": "2023-08-21T02:29:35.880532Z" + "iopub.execute_input": "2023-08-22T07:00:20.501016Z", + "iopub.status.busy": "2023-08-22T07:00:20.500451Z", + "iopub.status.idle": "2023-08-22T07:00:20.542279Z", + "shell.execute_reply": "2023-08-22T07:00:20.541055Z" } }, "outputs": [ @@ -560,10 +560,10 @@ "id": "3ca7417d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.892132Z", - "iopub.status.busy": "2023-08-21T02:29:35.891945Z", - "iopub.status.idle": "2023-08-21T02:29:35.906237Z", - "shell.execute_reply": "2023-08-21T02:29:35.903796Z" + "iopub.execute_input": "2023-08-22T07:00:20.547883Z", + "iopub.status.busy": "2023-08-22T07:00:20.546804Z", + "iopub.status.idle": "2023-08-22T07:00:20.558851Z", + "shell.execute_reply": "2023-08-22T07:00:20.557712Z" }, "lines_to_next_cell": 2 }, @@ -669,10 +669,10 @@ "id": "caadfcc3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.910461Z", - "iopub.status.busy": "2023-08-21T02:29:35.909732Z", - "iopub.status.idle": "2023-08-21T02:29:35.916845Z", - "shell.execute_reply": "2023-08-21T02:29:35.914913Z" + "iopub.execute_input": "2023-08-22T07:00:20.563550Z", + "iopub.status.busy": "2023-08-22T07:00:20.562723Z", + "iopub.status.idle": "2023-08-22T07:00:20.568989Z", + "shell.execute_reply": "2023-08-22T07:00:20.568096Z" }, "lines_to_next_cell": 2 }, @@ -710,10 +710,10 @@ "id": "92de2600", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.921712Z", - "iopub.status.busy": "2023-08-21T02:29:35.921332Z", - "iopub.status.idle": "2023-08-21T02:29:35.972191Z", - "shell.execute_reply": "2023-08-21T02:29:35.968927Z" + "iopub.execute_input": "2023-08-22T07:00:20.574149Z", + "iopub.status.busy": "2023-08-22T07:00:20.573276Z", + "iopub.status.idle": "2023-08-22T07:00:20.605121Z", + "shell.execute_reply": "2023-08-22T07:00:20.604136Z" }, "lines_to_next_cell": 2 }, @@ -820,10 +820,10 @@ "id": "a4452162", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:35.977098Z", - "iopub.status.busy": "2023-08-21T02:29:35.976606Z", - "iopub.status.idle": "2023-08-21T02:29:36.071648Z", - "shell.execute_reply": "2023-08-21T02:29:36.069969Z" + "iopub.execute_input": "2023-08-22T07:00:20.609844Z", + "iopub.status.busy": "2023-08-22T07:00:20.609519Z", + "iopub.status.idle": "2023-08-22T07:00:20.635472Z", + "shell.execute_reply": "2023-08-22T07:00:20.634538Z" }, "lines_to_next_cell": 2 }, @@ -933,10 +933,10 @@ "id": "5b317cb2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.079783Z", - "iopub.status.busy": "2023-08-21T02:29:36.079376Z", - "iopub.status.idle": "2023-08-21T02:29:36.089301Z", - "shell.execute_reply": "2023-08-21T02:29:36.086171Z" + "iopub.execute_input": "2023-08-22T07:00:20.644474Z", + "iopub.status.busy": "2023-08-22T07:00:20.644125Z", + "iopub.status.idle": "2023-08-22T07:00:20.652869Z", + "shell.execute_reply": "2023-08-22T07:00:20.652118Z" } }, "outputs": [], @@ -960,10 +960,10 @@ "id": "ba682884", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.096542Z", - "iopub.status.busy": "2023-08-21T02:29:36.096230Z", - "iopub.status.idle": "2023-08-21T02:29:36.294776Z", - "shell.execute_reply": "2023-08-21T02:29:36.292034Z" + "iopub.execute_input": "2023-08-22T07:00:20.659807Z", + "iopub.status.busy": "2023-08-22T07:00:20.659436Z", + "iopub.status.idle": "2023-08-22T07:00:20.794764Z", + "shell.execute_reply": "2023-08-22T07:00:20.793804Z" }, "lines_to_next_cell": 0 }, @@ -1025,10 +1025,10 @@ "id": "84c211b3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.301026Z", - "iopub.status.busy": "2023-08-21T02:29:36.300636Z", - "iopub.status.idle": "2023-08-21T02:29:36.330683Z", - "shell.execute_reply": "2023-08-21T02:29:36.329634Z" + "iopub.execute_input": "2023-08-22T07:00:20.802014Z", + "iopub.status.busy": "2023-08-22T07:00:20.801524Z", + "iopub.status.idle": "2023-08-22T07:00:20.820547Z", + "shell.execute_reply": "2023-08-22T07:00:20.819213Z" }, "lines_to_next_cell": 2 }, @@ -1154,10 +1154,10 @@ "id": "60466a94", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.335194Z", - "iopub.status.busy": "2023-08-21T02:29:36.334390Z", - "iopub.status.idle": "2023-08-21T02:29:36.342883Z", - "shell.execute_reply": "2023-08-21T02:29:36.341853Z" + "iopub.execute_input": "2023-08-22T07:00:20.827052Z", + "iopub.status.busy": "2023-08-22T07:00:20.826622Z", + "iopub.status.idle": "2023-08-22T07:00:20.840226Z", + "shell.execute_reply": "2023-08-22T07:00:20.838401Z" }, "lines_to_next_cell": 0 }, @@ -1197,10 +1197,10 @@ "id": "ff6d9fcb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.348667Z", - "iopub.status.busy": "2023-08-21T02:29:36.347888Z", - "iopub.status.idle": "2023-08-21T02:29:36.376193Z", - "shell.execute_reply": "2023-08-21T02:29:36.375375Z" + "iopub.execute_input": "2023-08-22T07:00:20.847470Z", + "iopub.status.busy": "2023-08-22T07:00:20.846698Z", + "iopub.status.idle": "2023-08-22T07:00:20.876785Z", + "shell.execute_reply": "2023-08-22T07:00:20.874921Z" }, "lines_to_next_cell": 0 }, @@ -1323,10 +1323,10 @@ "id": "2a206718", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.386789Z", - "iopub.status.busy": "2023-08-21T02:29:36.386044Z", - "iopub.status.idle": "2023-08-21T02:29:36.418061Z", - "shell.execute_reply": "2023-08-21T02:29:36.416433Z" + "iopub.execute_input": "2023-08-22T07:00:20.884203Z", + "iopub.status.busy": "2023-08-22T07:00:20.883987Z", + "iopub.status.idle": "2023-08-22T07:00:20.912686Z", + "shell.execute_reply": "2023-08-22T07:00:20.910961Z" } }, "outputs": [ @@ -1459,10 +1459,10 @@ "id": "766241f2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.425365Z", - "iopub.status.busy": "2023-08-21T02:29:36.424580Z", - "iopub.status.idle": "2023-08-21T02:29:36.432225Z", - "shell.execute_reply": "2023-08-21T02:29:36.431397Z" + "iopub.execute_input": "2023-08-22T07:00:20.920607Z", + "iopub.status.busy": "2023-08-22T07:00:20.918963Z", + "iopub.status.idle": "2023-08-22T07:00:20.929300Z", + "shell.execute_reply": "2023-08-22T07:00:20.928186Z" }, "lines_to_next_cell": 0 }, @@ -1504,10 +1504,10 @@ "id": "71b3ac84", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.443753Z", - "iopub.status.busy": "2023-08-21T02:29:36.443393Z", - "iopub.status.idle": "2023-08-21T02:29:36.490069Z", - "shell.execute_reply": "2023-08-21T02:29:36.489170Z" + "iopub.execute_input": "2023-08-22T07:00:20.943822Z", + "iopub.status.busy": "2023-08-22T07:00:20.943014Z", + "iopub.status.idle": "2023-08-22T07:00:20.968025Z", + "shell.execute_reply": "2023-08-22T07:00:20.966003Z" }, "lines_to_next_cell": 0 }, @@ -1638,10 +1638,10 @@ "id": "eb35ef98", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.498313Z", - "iopub.status.busy": "2023-08-21T02:29:36.498133Z", - "iopub.status.idle": "2023-08-21T02:29:36.532175Z", - "shell.execute_reply": "2023-08-21T02:29:36.530924Z" + "iopub.execute_input": "2023-08-22T07:00:20.975032Z", + "iopub.status.busy": "2023-08-22T07:00:20.974773Z", + "iopub.status.idle": "2023-08-22T07:00:21.001621Z", + "shell.execute_reply": "2023-08-22T07:00:20.999410Z" }, "lines_to_next_cell": 0 }, @@ -1755,10 +1755,10 @@ "id": "78b393fe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.541740Z", - "iopub.status.busy": "2023-08-21T02:29:36.541236Z", - "iopub.status.idle": "2023-08-21T02:29:36.729893Z", - "shell.execute_reply": "2023-08-21T02:29:36.728852Z" + "iopub.execute_input": "2023-08-22T07:00:21.009111Z", + "iopub.status.busy": "2023-08-22T07:00:21.008401Z", + "iopub.status.idle": "2023-08-22T07:00:21.127979Z", + "shell.execute_reply": "2023-08-22T07:00:21.126680Z" } }, "outputs": [ @@ -1803,10 +1803,10 @@ "id": "cb014cc6", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.738629Z", - "iopub.status.busy": "2023-08-21T02:29:36.738138Z", - "iopub.status.idle": "2023-08-21T02:29:36.783309Z", - "shell.execute_reply": "2023-08-21T02:29:36.781945Z" + "iopub.execute_input": "2023-08-22T07:00:21.135155Z", + "iopub.status.busy": "2023-08-22T07:00:21.134355Z", + "iopub.status.idle": "2023-08-22T07:00:21.153025Z", + "shell.execute_reply": "2023-08-22T07:00:21.152246Z" } }, "outputs": [ @@ -1849,10 +1849,10 @@ "id": "106dc178", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:36.791911Z", - "iopub.status.busy": "2023-08-21T02:29:36.791104Z", - "iopub.status.idle": "2023-08-21T02:29:37.275891Z", - "shell.execute_reply": "2023-08-21T02:29:37.275551Z" + "iopub.execute_input": "2023-08-22T07:00:21.159707Z", + "iopub.status.busy": "2023-08-22T07:00:21.159506Z", + "iopub.status.idle": "2023-08-22T07:00:21.446559Z", + "shell.execute_reply": "2023-08-22T07:00:21.445625Z" } }, "outputs": [ @@ -1895,10 +1895,10 @@ "id": "d0334df0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:37.277695Z", - "iopub.status.busy": "2023-08-21T02:29:37.277558Z", - "iopub.status.idle": "2023-08-21T02:29:38.159528Z", - "shell.execute_reply": "2023-08-21T02:29:38.158089Z" + "iopub.execute_input": "2023-08-22T07:00:21.454580Z", + "iopub.status.busy": "2023-08-22T07:00:21.453805Z", + "iopub.status.idle": "2023-08-22T07:00:21.741596Z", + "shell.execute_reply": "2023-08-22T07:00:21.740381Z" } }, "outputs": [ @@ -1950,10 +1950,10 @@ "id": "e00ec554", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:38.167367Z", - "iopub.status.busy": "2023-08-21T02:29:38.166718Z", - "iopub.status.idle": "2023-08-21T02:29:38.206501Z", - "shell.execute_reply": "2023-08-21T02:29:38.203694Z" + "iopub.execute_input": "2023-08-22T07:00:21.748135Z", + "iopub.status.busy": "2023-08-22T07:00:21.747093Z", + "iopub.status.idle": "2023-08-22T07:00:21.771304Z", + "shell.execute_reply": "2023-08-22T07:00:21.768895Z" }, "lines_to_next_cell": 2 }, @@ -1992,10 +1992,10 @@ "id": "28e301a2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:38.216727Z", - "iopub.status.busy": "2023-08-21T02:29:38.215858Z", - "iopub.status.idle": "2023-08-21T02:29:38.814109Z", - "shell.execute_reply": "2023-08-21T02:29:38.813789Z" + "iopub.execute_input": "2023-08-22T07:00:21.776636Z", + "iopub.status.busy": "2023-08-22T07:00:21.775975Z", + "iopub.status.idle": "2023-08-22T07:00:22.088656Z", + "shell.execute_reply": "2023-08-22T07:00:22.087101Z" } }, "outputs": [ @@ -2054,10 +2054,10 @@ "id": "7988af75", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:38.815771Z", - "iopub.status.busy": "2023-08-21T02:29:38.815666Z", - "iopub.status.idle": "2023-08-21T02:29:38.846626Z", - "shell.execute_reply": "2023-08-21T02:29:38.845296Z" + "iopub.execute_input": "2023-08-22T07:00:22.093999Z", + "iopub.status.busy": "2023-08-22T07:00:22.092990Z", + "iopub.status.idle": "2023-08-22T07:00:22.104732Z", + "shell.execute_reply": "2023-08-22T07:00:22.103894Z" }, "lines_to_next_cell": 0 }, @@ -2093,10 +2093,10 @@ "id": "1a4803f3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:38.852512Z", - "iopub.status.busy": "2023-08-21T02:29:38.851848Z", - "iopub.status.idle": "2023-08-21T02:29:39.089982Z", - "shell.execute_reply": "2023-08-21T02:29:39.089296Z" + "iopub.execute_input": "2023-08-22T07:00:22.111630Z", + "iopub.status.busy": "2023-08-22T07:00:22.109538Z", + "iopub.status.idle": "2023-08-22T07:00:22.238185Z", + "shell.execute_reply": "2023-08-22T07:00:22.237011Z" }, "lines_to_next_cell": 0 }, @@ -2156,10 +2156,10 @@ "id": "dc655431", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:39.094771Z", - "iopub.status.busy": "2023-08-21T02:29:39.093629Z", - "iopub.status.idle": "2023-08-21T02:29:39.227174Z", - "shell.execute_reply": "2023-08-21T02:29:39.226787Z" + "iopub.execute_input": "2023-08-22T07:00:22.242955Z", + "iopub.status.busy": "2023-08-22T07:00:22.242470Z", + "iopub.status.idle": "2023-08-22T07:00:22.377106Z", + "shell.execute_reply": "2023-08-22T07:00:22.375348Z" } }, "outputs": [ @@ -2217,10 +2217,10 @@ "id": "90ea4ff0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:39.228985Z", - "iopub.status.busy": "2023-08-21T02:29:39.228844Z", - "iopub.status.idle": "2023-08-21T02:29:39.289822Z", - "shell.execute_reply": "2023-08-21T02:29:39.286542Z" + "iopub.execute_input": "2023-08-22T07:00:22.382764Z", + "iopub.status.busy": "2023-08-22T07:00:22.381824Z", + "iopub.status.idle": "2023-08-22T07:00:22.403585Z", + "shell.execute_reply": "2023-08-22T07:00:22.401813Z" }, "lines_to_next_cell": 0 }, @@ -2254,10 +2254,10 @@ "id": "104bc542", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:39.297216Z", - "iopub.status.busy": "2023-08-21T02:29:39.295538Z", - "iopub.status.idle": "2023-08-21T02:29:39.509987Z", - "shell.execute_reply": "2023-08-21T02:29:39.505606Z" + "iopub.execute_input": "2023-08-22T07:00:22.408939Z", + "iopub.status.busy": "2023-08-22T07:00:22.408540Z", + "iopub.status.idle": "2023-08-22T07:00:22.524209Z", + "shell.execute_reply": "2023-08-22T07:00:22.522972Z" } }, "outputs": [ @@ -2297,10 +2297,10 @@ "id": "d5884f39", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:39.515536Z", - "iopub.status.busy": "2023-08-21T02:29:39.514906Z", - "iopub.status.idle": "2023-08-21T02:29:39.590528Z", - "shell.execute_reply": "2023-08-21T02:29:39.587475Z" + "iopub.execute_input": "2023-08-22T07:00:22.529074Z", + "iopub.status.busy": "2023-08-22T07:00:22.528274Z", + "iopub.status.idle": "2023-08-22T07:00:22.562860Z", + "shell.execute_reply": "2023-08-22T07:00:22.561973Z" }, "lines_to_next_cell": 0 }, @@ -2330,10 +2330,10 @@ "id": "9155767c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:39.595864Z", - "iopub.status.busy": "2023-08-21T02:29:39.595093Z", - "iopub.status.idle": "2023-08-21T02:29:39.751046Z", - "shell.execute_reply": "2023-08-21T02:29:39.747430Z" + "iopub.execute_input": "2023-08-22T07:00:22.569210Z", + "iopub.status.busy": "2023-08-22T07:00:22.568849Z", + "iopub.status.idle": "2023-08-22T07:00:22.677881Z", + "shell.execute_reply": "2023-08-22T07:00:22.676932Z" }, "lines_to_next_cell": 0 }, @@ -2383,10 +2383,10 @@ "id": "048524d1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:39.765191Z", - "iopub.status.busy": "2023-08-21T02:29:39.764636Z", - "iopub.status.idle": "2023-08-21T02:29:39.875769Z", - "shell.execute_reply": "2023-08-21T02:29:39.875407Z" + "iopub.execute_input": "2023-08-22T07:00:22.685366Z", + "iopub.status.busy": "2023-08-22T07:00:22.684753Z", + "iopub.status.idle": "2023-08-22T07:00:22.790525Z", + "shell.execute_reply": "2023-08-22T07:00:22.789666Z" } }, "outputs": [ @@ -2430,10 +2430,10 @@ "id": "3d632d24", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:39.878255Z", - "iopub.status.busy": "2023-08-21T02:29:39.878128Z", - "iopub.status.idle": "2023-08-21T02:29:40.030675Z", - "shell.execute_reply": "2023-08-21T02:29:40.030077Z" + "iopub.execute_input": "2023-08-22T07:00:22.797262Z", + "iopub.status.busy": "2023-08-22T07:00:22.796333Z", + "iopub.status.idle": "2023-08-22T07:00:22.839603Z", + "shell.execute_reply": "2023-08-22T07:00:22.837625Z" } }, "outputs": [ @@ -2477,10 +2477,10 @@ "id": "f5e21a13", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:40.036373Z", - "iopub.status.busy": "2023-08-21T02:29:40.035626Z", - "iopub.status.idle": "2023-08-21T02:29:40.048244Z", - "shell.execute_reply": "2023-08-21T02:29:40.047103Z" + "iopub.execute_input": "2023-08-22T07:00:22.850660Z", + "iopub.status.busy": "2023-08-22T07:00:22.849974Z", + "iopub.status.idle": "2023-08-22T07:00:22.858653Z", + "shell.execute_reply": "2023-08-22T07:00:22.857543Z" }, "lines_to_next_cell": 0 }, @@ -2584,10 +2584,10 @@ "id": "6bc84bd9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:40.053032Z", - "iopub.status.busy": "2023-08-21T02:29:40.052718Z", - "iopub.status.idle": "2023-08-21T02:29:40.121414Z", - "shell.execute_reply": "2023-08-21T02:29:40.119305Z" + "iopub.execute_input": "2023-08-22T07:00:22.866946Z", + "iopub.status.busy": "2023-08-22T07:00:22.866205Z", + "iopub.status.idle": "2023-08-22T07:00:22.902537Z", + "shell.execute_reply": "2023-08-22T07:00:22.901746Z" } }, "outputs": [ @@ -2694,10 +2694,10 @@ "id": "dd9e0491", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:40.127853Z", - "iopub.status.busy": "2023-08-21T02:29:40.126917Z", - "iopub.status.idle": "2023-08-21T02:29:40.134348Z", - "shell.execute_reply": "2023-08-21T02:29:40.133225Z" + "iopub.execute_input": "2023-08-22T07:00:22.910365Z", + "iopub.status.busy": "2023-08-22T07:00:22.909672Z", + "iopub.status.idle": "2023-08-22T07:00:22.916982Z", + "shell.execute_reply": "2023-08-22T07:00:22.915954Z" } }, "outputs": [ @@ -2736,7 +2736,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_81087/2135516388.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n", + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_84754/2135516388.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n", " \n", "Please do not make inferences based on these values! \n", "\n", @@ -2767,10 +2767,10 @@ "id": "cb3cb4ef", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:40.140991Z", - "iopub.status.busy": "2023-08-21T02:29:40.139059Z", - "iopub.status.idle": "2023-08-21T02:29:40.156398Z", - "shell.execute_reply": "2023-08-21T02:29:40.154898Z" + "iopub.execute_input": "2023-08-22T07:00:22.924618Z", + "iopub.status.busy": "2023-08-22T07:00:22.924291Z", + "iopub.status.idle": "2023-08-22T07:00:22.938562Z", + "shell.execute_reply": "2023-08-22T07:00:22.937364Z" } }, "outputs": [], @@ -2793,10 +2793,10 @@ "id": "583711cb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:40.161933Z", - "iopub.status.busy": "2023-08-21T02:29:40.161044Z", - "iopub.status.idle": "2023-08-21T02:29:41.074609Z", - "shell.execute_reply": "2023-08-21T02:29:41.073657Z" + "iopub.execute_input": "2023-08-22T07:00:22.946138Z", + "iopub.status.busy": "2023-08-22T07:00:22.945678Z", + "iopub.status.idle": "2023-08-22T07:00:23.056404Z", + "shell.execute_reply": "2023-08-22T07:00:23.054342Z" }, "lines_to_next_cell": 2 }, @@ -2827,10 +2827,10 @@ "id": "259d273b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.077991Z", - "iopub.status.busy": "2023-08-21T02:29:41.077451Z", - "iopub.status.idle": "2023-08-21T02:29:41.210787Z", - "shell.execute_reply": "2023-08-21T02:29:41.209817Z" + "iopub.execute_input": "2023-08-22T07:00:23.062645Z", + "iopub.status.busy": "2023-08-22T07:00:23.062312Z", + "iopub.status.idle": "2023-08-22T07:00:23.191981Z", + "shell.execute_reply": "2023-08-22T07:00:23.190477Z" }, "lines_to_next_cell": 0 }, @@ -2871,10 +2871,10 @@ "id": "71dde739", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.214250Z", - "iopub.status.busy": "2023-08-21T02:29:41.213993Z", - "iopub.status.idle": "2023-08-21T02:29:41.230586Z", - "shell.execute_reply": "2023-08-21T02:29:41.229987Z" + "iopub.execute_input": "2023-08-22T07:00:23.199542Z", + "iopub.status.busy": "2023-08-22T07:00:23.199201Z", + "iopub.status.idle": "2023-08-22T07:00:23.217281Z", + "shell.execute_reply": "2023-08-22T07:00:23.216211Z" }, "lines_to_next_cell": 0 }, @@ -2980,10 +2980,10 @@ "id": "126b4433", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.236028Z", - "iopub.status.busy": "2023-08-21T02:29:41.234087Z", - "iopub.status.idle": "2023-08-21T02:29:41.242018Z", - "shell.execute_reply": "2023-08-21T02:29:41.240913Z" + "iopub.execute_input": "2023-08-22T07:00:23.225140Z", + "iopub.status.busy": "2023-08-22T07:00:23.224675Z", + "iopub.status.idle": "2023-08-22T07:00:23.231622Z", + "shell.execute_reply": "2023-08-22T07:00:23.230728Z" }, "lines_to_next_cell": 0 }, @@ -3014,10 +3014,10 @@ "id": "71357343", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.246132Z", - "iopub.status.busy": "2023-08-21T02:29:41.245849Z", - "iopub.status.idle": "2023-08-21T02:29:41.516225Z", - "shell.execute_reply": "2023-08-21T02:29:41.513222Z" + "iopub.execute_input": "2023-08-22T07:00:23.238285Z", + "iopub.status.busy": "2023-08-22T07:00:23.237444Z", + "iopub.status.idle": "2023-08-22T07:00:23.300732Z", + "shell.execute_reply": "2023-08-22T07:00:23.299079Z" }, "lines_to_next_cell": 2 }, @@ -3057,10 +3057,10 @@ "id": "568fe30f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.521913Z", - "iopub.status.busy": "2023-08-21T02:29:41.521643Z", - "iopub.status.idle": "2023-08-21T02:29:41.650531Z", - "shell.execute_reply": "2023-08-21T02:29:41.648645Z" + "iopub.execute_input": "2023-08-22T07:00:23.308132Z", + "iopub.status.busy": "2023-08-22T07:00:23.307471Z", + "iopub.status.idle": "2023-08-22T07:00:23.412833Z", + "shell.execute_reply": "2023-08-22T07:00:23.411958Z" } }, "outputs": [ @@ -3091,10 +3091,10 @@ "id": "56f3acef", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.656894Z", - "iopub.status.busy": "2023-08-21T02:29:41.656175Z", - "iopub.status.idle": "2023-08-21T02:29:41.778189Z", - "shell.execute_reply": "2023-08-21T02:29:41.777902Z" + "iopub.execute_input": "2023-08-22T07:00:23.419875Z", + "iopub.status.busy": "2023-08-22T07:00:23.419189Z", + "iopub.status.idle": "2023-08-22T07:00:23.528901Z", + "shell.execute_reply": "2023-08-22T07:00:23.527531Z" } }, "outputs": [ @@ -3124,10 +3124,10 @@ "id": "74d23615", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.779880Z", - "iopub.status.busy": "2023-08-21T02:29:41.779764Z", - "iopub.status.idle": "2023-08-21T02:29:41.891093Z", - "shell.execute_reply": "2023-08-21T02:29:41.890788Z" + "iopub.execute_input": "2023-08-22T07:00:23.533975Z", + "iopub.status.busy": "2023-08-22T07:00:23.533655Z", + "iopub.status.idle": "2023-08-22T07:00:23.643883Z", + "shell.execute_reply": "2023-08-22T07:00:23.643400Z" }, "lines_to_next_cell": 2 }, @@ -3172,10 +3172,10 @@ "id": "5a0046bf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:41.892708Z", - "iopub.status.busy": "2023-08-21T02:29:41.892590Z", - "iopub.status.idle": "2023-08-21T02:29:42.034991Z", - "shell.execute_reply": "2023-08-21T02:29:42.034633Z" + "iopub.execute_input": "2023-08-22T07:00:23.646013Z", + "iopub.status.busy": "2023-08-22T07:00:23.645883Z", + "iopub.status.idle": "2023-08-22T07:00:23.774046Z", + "shell.execute_reply": "2023-08-22T07:00:23.773509Z" }, "lines_to_next_cell": 0 }, @@ -3222,7 +3222,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -3235,7 +3235,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch08-baggboost-lab.Rmd b/Ch08-baggboost-lab.Rmd index 50e480c..67a23d6 100644 --- a/Ch08-baggboost-lab.Rmd +++ b/Ch08-baggboost-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch08-baggboost-lab.ipynb b/Ch08-baggboost-lab.ipynb index 0aaa6ac..b378a78 100644 --- a/Ch08-baggboost-lab.ipynb +++ b/Ch08-baggboost-lab.ipynb @@ -26,10 +26,10 @@ "id": "5061d7d5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:43.521031Z", - "iopub.status.busy": "2023-08-21T02:29:43.520759Z", - "iopub.status.idle": "2023-08-21T02:29:44.874525Z", - "shell.execute_reply": "2023-08-21T02:29:44.874222Z" + "iopub.execute_input": "2023-08-22T07:00:27.086529Z", + "iopub.status.busy": "2023-08-22T07:00:27.086444Z", + "iopub.status.idle": "2023-08-22T07:00:28.125805Z", + "shell.execute_reply": "2023-08-22T07:00:28.125477Z" }, "lines_to_next_cell": 0 }, @@ -58,10 +58,10 @@ "id": "747b056a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:44.876483Z", - "iopub.status.busy": "2023-08-21T02:29:44.876317Z", - "iopub.status.idle": "2023-08-21T02:29:44.941466Z", - "shell.execute_reply": "2023-08-21T02:29:44.941148Z" + "iopub.execute_input": "2023-08-22T07:00:28.127941Z", + "iopub.status.busy": "2023-08-22T07:00:28.127762Z", + "iopub.status.idle": "2023-08-22T07:00:28.677332Z", + "shell.execute_reply": "2023-08-22T07:00:28.676967Z" }, "lines_to_next_cell": 2 }, @@ -106,10 +106,10 @@ "id": "a29167fd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:44.943319Z", - "iopub.status.busy": "2023-08-21T02:29:44.943207Z", - "iopub.status.idle": "2023-08-21T02:29:44.948609Z", - "shell.execute_reply": "2023-08-21T02:29:44.948363Z" + "iopub.execute_input": "2023-08-22T07:00:28.679410Z", + "iopub.status.busy": "2023-08-22T07:00:28.679295Z", + "iopub.status.idle": "2023-08-22T07:00:28.684543Z", + "shell.execute_reply": "2023-08-22T07:00:28.684233Z" } }, "outputs": [], @@ -137,10 +137,10 @@ "id": "9fc6c8b9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:44.950122Z", - "iopub.status.busy": "2023-08-21T02:29:44.950017Z", - "iopub.status.idle": "2023-08-21T02:29:44.964042Z", - "shell.execute_reply": "2023-08-21T02:29:44.963794Z" + "iopub.execute_input": "2023-08-22T07:00:28.686277Z", + "iopub.status.busy": "2023-08-22T07:00:28.686146Z", + "iopub.status.idle": "2023-08-22T07:00:28.699975Z", + "shell.execute_reply": "2023-08-22T07:00:28.699627Z" }, "lines_to_next_cell": 0 }, @@ -172,10 +172,10 @@ "id": "533f0949", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:44.965639Z", - "iopub.status.busy": "2023-08-21T02:29:44.965555Z", - "iopub.status.idle": "2023-08-21T02:29:44.972079Z", - "shell.execute_reply": "2023-08-21T02:29:44.971812Z" + "iopub.execute_input": "2023-08-22T07:00:28.702236Z", + "iopub.status.busy": "2023-08-22T07:00:28.702106Z", + "iopub.status.idle": "2023-08-22T07:00:28.707600Z", + "shell.execute_reply": "2023-08-22T07:00:28.707288Z" }, "lines_to_next_cell": 2 }, @@ -224,10 +224,10 @@ "id": "c4a8718e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:44.973649Z", - "iopub.status.busy": "2023-08-21T02:29:44.973541Z", - "iopub.status.idle": "2023-08-21T02:29:44.976459Z", - "shell.execute_reply": "2023-08-21T02:29:44.976196Z" + "iopub.execute_input": "2023-08-22T07:00:28.709124Z", + "iopub.status.busy": "2023-08-22T07:00:28.709024Z", + "iopub.status.idle": "2023-08-22T07:00:28.711727Z", + "shell.execute_reply": "2023-08-22T07:00:28.711484Z" }, "lines_to_next_cell": 2 }, @@ -271,10 +271,10 @@ "id": "2fe92cb1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:44.977909Z", - "iopub.status.busy": "2023-08-21T02:29:44.977813Z", - "iopub.status.idle": "2023-08-21T02:29:44.980904Z", - "shell.execute_reply": "2023-08-21T02:29:44.980648Z" + "iopub.execute_input": "2023-08-22T07:00:28.713483Z", + "iopub.status.busy": "2023-08-22T07:00:28.713358Z", + "iopub.status.idle": "2023-08-22T07:00:28.716712Z", + "shell.execute_reply": "2023-08-22T07:00:28.716377Z" } }, "outputs": [ @@ -314,10 +314,10 @@ "id": "823745dc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:44.982460Z", - "iopub.status.busy": "2023-08-21T02:29:44.982347Z", - "iopub.status.idle": "2023-08-21T02:29:45.271601Z", - "shell.execute_reply": "2023-08-21T02:29:45.271198Z" + "iopub.execute_input": "2023-08-22T07:00:28.718214Z", + "iopub.status.busy": "2023-08-22T07:00:28.718128Z", + "iopub.status.idle": "2023-08-22T07:00:28.972065Z", + "shell.execute_reply": "2023-08-22T07:00:28.971661Z" }, "lines_to_next_cell": 0 }, @@ -362,10 +362,10 @@ "id": "38ec5f13", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:45.273437Z", - "iopub.status.busy": "2023-08-21T02:29:45.273325Z", - "iopub.status.idle": "2023-08-21T02:29:45.276242Z", - "shell.execute_reply": "2023-08-21T02:29:45.275887Z" + "iopub.execute_input": "2023-08-22T07:00:28.974012Z", + "iopub.status.busy": "2023-08-22T07:00:28.973872Z", + "iopub.status.idle": "2023-08-22T07:00:28.976664Z", + "shell.execute_reply": "2023-08-22T07:00:28.976277Z" } }, "outputs": [ @@ -427,10 +427,10 @@ "id": "3959f39a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:45.277931Z", - "iopub.status.busy": "2023-08-21T02:29:45.277817Z", - "iopub.status.idle": "2023-08-21T02:29:45.284043Z", - "shell.execute_reply": "2023-08-21T02:29:45.283649Z" + "iopub.execute_input": "2023-08-22T07:00:28.978468Z", + "iopub.status.busy": "2023-08-22T07:00:28.978343Z", + "iopub.status.idle": "2023-08-22T07:00:28.984086Z", + "shell.execute_reply": "2023-08-22T07:00:28.983816Z" }, "lines_to_next_cell": 0 }, @@ -483,10 +483,10 @@ "id": "201c4690", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:45.286011Z", - "iopub.status.busy": "2023-08-21T02:29:45.285866Z", - "iopub.status.idle": "2023-08-21T02:29:45.288396Z", - "shell.execute_reply": "2023-08-21T02:29:45.288038Z" + "iopub.execute_input": "2023-08-22T07:00:28.986102Z", + "iopub.status.busy": "2023-08-22T07:00:28.985936Z", + "iopub.status.idle": "2023-08-22T07:00:28.988314Z", + "shell.execute_reply": "2023-08-22T07:00:28.988003Z" }, "lines_to_next_cell": 0 }, @@ -516,10 +516,10 @@ "id": "a8dc5c3a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:45.290090Z", - "iopub.status.busy": "2023-08-21T02:29:45.289979Z", - "iopub.status.idle": "2023-08-21T02:29:45.293957Z", - "shell.execute_reply": "2023-08-21T02:29:45.293696Z" + "iopub.execute_input": "2023-08-22T07:00:28.990208Z", + "iopub.status.busy": "2023-08-22T07:00:28.990083Z", + "iopub.status.idle": "2023-08-22T07:00:28.994160Z", + "shell.execute_reply": "2023-08-22T07:00:28.993813Z" }, "lines_to_next_cell": 0 }, @@ -556,10 +556,10 @@ "id": "0cbe0d28", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:45.295541Z", - "iopub.status.busy": "2023-08-21T02:29:45.295458Z", - "iopub.status.idle": "2023-08-21T02:29:45.299182Z", - "shell.execute_reply": "2023-08-21T02:29:45.298807Z" + "iopub.execute_input": "2023-08-22T07:00:28.996199Z", + "iopub.status.busy": "2023-08-22T07:00:28.996086Z", + "iopub.status.idle": "2023-08-22T07:00:28.999517Z", + "shell.execute_reply": "2023-08-22T07:00:28.999166Z" }, "lines_to_next_cell": 0 }, @@ -586,10 +586,10 @@ "id": "ea649080", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:45.300868Z", - "iopub.status.busy": "2023-08-21T02:29:45.300771Z", - "iopub.status.idle": "2023-08-21T02:29:45.571746Z", - "shell.execute_reply": "2023-08-21T02:29:45.571364Z" + "iopub.execute_input": "2023-08-22T07:00:29.001372Z", + "iopub.status.busy": "2023-08-22T07:00:29.001246Z", + "iopub.status.idle": "2023-08-22T07:00:29.236635Z", + "shell.execute_reply": "2023-08-22T07:00:29.236312Z" }, "lines_to_next_cell": 0 }, @@ -629,10 +629,10 @@ "id": "e005da14", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:45.573701Z", - "iopub.status.busy": "2023-08-21T02:29:45.573563Z", - "iopub.status.idle": "2023-08-21T02:29:46.379024Z", - "shell.execute_reply": "2023-08-21T02:29:46.378601Z" + "iopub.execute_input": "2023-08-22T07:00:29.238455Z", + "iopub.status.busy": "2023-08-22T07:00:29.238325Z", + "iopub.status.idle": "2023-08-22T07:00:29.937364Z", + "shell.execute_reply": "2023-08-22T07:00:29.937016Z" }, "lines_to_next_cell": 0 }, @@ -671,10 +671,10 @@ "id": "23324f7e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.381085Z", - "iopub.status.busy": "2023-08-21T02:29:46.380960Z", - "iopub.status.idle": "2023-08-21T02:29:46.383318Z", - "shell.execute_reply": "2023-08-21T02:29:46.383057Z" + "iopub.execute_input": "2023-08-22T07:00:29.939343Z", + "iopub.status.busy": "2023-08-22T07:00:29.939187Z", + "iopub.status.idle": "2023-08-22T07:00:29.942069Z", + "shell.execute_reply": "2023-08-22T07:00:29.941716Z" }, "lines_to_next_cell": 0 }, @@ -711,10 +711,10 @@ "id": "6dcd8b37", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.385077Z", - "iopub.status.busy": "2023-08-21T02:29:46.384936Z", - "iopub.status.idle": "2023-08-21T02:29:46.392841Z", - "shell.execute_reply": "2023-08-21T02:29:46.392541Z" + "iopub.execute_input": "2023-08-22T07:00:29.943944Z", + "iopub.status.busy": "2023-08-22T07:00:29.943797Z", + "iopub.status.idle": "2023-08-22T07:00:29.951177Z", + "shell.execute_reply": "2023-08-22T07:00:29.950818Z" }, "lines_to_next_cell": 2 }, @@ -817,10 +817,10 @@ "id": "0459d3a9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.395016Z", - "iopub.status.busy": "2023-08-21T02:29:46.394693Z", - "iopub.status.idle": "2023-08-21T02:29:46.408492Z", - "shell.execute_reply": "2023-08-21T02:29:46.408187Z" + "iopub.execute_input": "2023-08-22T07:00:29.953197Z", + "iopub.status.busy": "2023-08-22T07:00:29.952962Z", + "iopub.status.idle": "2023-08-22T07:00:29.965364Z", + "shell.execute_reply": "2023-08-22T07:00:29.965021Z" } }, "outputs": [], @@ -847,10 +847,10 @@ "id": "34bf2864", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.411065Z", - "iopub.status.busy": "2023-08-21T02:29:46.410850Z", - "iopub.status.idle": "2023-08-21T02:29:46.413541Z", - "shell.execute_reply": "2023-08-21T02:29:46.413268Z" + "iopub.execute_input": "2023-08-22T07:00:29.967326Z", + "iopub.status.busy": "2023-08-22T07:00:29.967175Z", + "iopub.status.idle": "2023-08-22T07:00:29.969789Z", + "shell.execute_reply": "2023-08-22T07:00:29.969459Z" } }, "outputs": [], @@ -878,16 +878,16 @@ "id": "dd0dfd8a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.415108Z", - "iopub.status.busy": "2023-08-21T02:29:46.414996Z", - "iopub.status.idle": "2023-08-21T02:29:46.704739Z", - "shell.execute_reply": "2023-08-21T02:29:46.704318Z" + "iopub.execute_input": "2023-08-22T07:00:29.971954Z", + "iopub.status.busy": "2023-08-22T07:00:29.971825Z", + "iopub.status.idle": "2023-08-22T07:00:30.219764Z", + "shell.execute_reply": "2023-08-22T07:00:30.219356Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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atm2bihUrprlz51qvGz9+vMLCwp44TXrixIkqVKhQckcFACDZUWwBAA4pIiJC5cuXl4+Pj8xmsyIjI7Vt2zb5+PioQoUKql27tu7duydJMpvN6tmzpwICAmQ2mzV//nwFBASoaNGiOnHihPU2Xbp0UeXKleXn56fLly/He7yYmBh16NBBvr6+8vb21q5duyRJI0aMUKlSpeTr6/tK7/2aKlUq68d37txR4cKFrX/u1auXKlSoYH2fX+nR++tu3rz5iV1cAAAcEcUWAOCQQkJCVLlyZYWGhiosLEwZM2ZU8eLFFRoaqu3bt6tgwYJatGiR9faVKlVScHCwPD09tXfvXgUHB6t3796aMWOG9TalS5fWpk2b1KJFC3311VfxHm/69OnKmzevQkJCtHz5cvXq1UuSNG/ePIWGhiokJEQ9evR4Imf9+vVlNpvj/Rf3/rP/NnnyZBUpUkQ7duywFtuvv/5ae/bs0erVq/XNN9/o5MmTkqQvvvhC/fr1e6WvIQAA9oIXjwIAOKTGjRtr5MiRatGihXLmzKnAwEAdOXJEgwcP1v379/X3338rffr01tsXL15ckpQ9e3blzZvX+vHWrVuttylXrpz1/8uXL4/3eIcPH9bOnTu1YcMGSY92TCVpwoQJ6tKlix48eKBPPvlE3t7e8Y57kVdZ7tSpkzp16qRRo0bpq6++0pgxY5Q1a1ZJUsaMGRUQEKCDBw8qZcqUOnv2rCpWrKgzZ84k+P4BALBXFFsAgENycXHRyJEjJUnt2rVTcHCwpk2bps8//1xly5ZV37599fhbuZtMpqd+/Phtdu/eLU9PT+3evVv58+eP93iFCxeWp6enevbsKenRCzdJUtmyZeXn56dz586pXr162r9/f7zj6tevr+vXr8e7zNPTU9OmTYt3WVRUlNzd3SVJmTJlUlRUlKRHBTpjxoyKiYnRjh071KRJEx04cEAXL15U1apVdeHCBd2/f19FihRR3bp1E/4FBADAjlBsAQAOac2aNRo/frxcXV3l5uYmb29v3blzRx999JEKFCig9OnTx9uxTYgDBw5o9uzZevjwoRYsWBDvug4dOqhr167y8fGRJBUrVkxjx45VvXr1FBUVpaioKHXp0uWJ+0zoju3w4cO1c+dOSY/eTzfuFOkmTZrozp07iomJUdOmTfXBBx/ogw8+sP5u7axZs3T+/HlKLQDAoZksjy9FAwCQTA4cOKDixYtr//798vLyMjrOfzKbzQoKClL27NmNjmKT7O37CQBwLLx4FAAAAADArnEqMgAACRAWFmZ0BAAA8Azs2AIAAAAA7BrFFgDg9CIiIuTv759sjxcYGKjSpUurfPny6t69u/WVlz/++GOVKVNGZcqU0ahRo5447ty5c/He7zZlypSKjIy0Xh8eHq6UKVNqx44dkh7tMr/55pvW2+/Zsyd5PkEAAJIZpyIDAJDMmjdvrsDAQEmP3m83JCREfn5+6t27t/Lly6fY2FiVL19ejRo1sr6nriS988471lOid+7cqREjRihTpkzW6wMDA2U2m+M9Vo0aNZ546yAAABwNO7YAAIfUu3dvLV26VJL04MEDvf/++4qJidHAgQPl6+srLy8vTZ48+Ynj2rZtG2/Hs3379pKk33//Xf7+/vL19VWjRo109+7dl86WL18+68dubm5ydXWNd7mLi4tSpEhhvfxp5syZo1atWln/vG3bNuXKlUtvv/12vNtt3LhR3t7e6ty58ytlBgDAllFsAQAOqW3btpo9e7YkKTg4WL6+vkqZMqUGDRqkkJAQ7dq1S+PGjVNMTEyC7q9z586aMWOGQkJCZDabNXXq1HjXR0dHxztNOO6/wYMHP/M+w8LCdP78eVWsWDHe5UFBQcqRI4dy5cr11OOio6O1fv1663vTWiwWjRw5Uv369Yt3u+LFi+vEiRPasWOHsmbNqtGjRyfocwUAwN5wKjIAwCEVKVJEV65c0eXLlzV79mwNGDBAkjR58mStWLFCrq6uunz5si5fvhzvOJPJZP348bd6P3LkiFq3bi1Jun///hOn/KZKleqFXjn5wIEDGjBggNasWSMXl/9bZ16/fr3mzJmjVatWPfPY1atXy9fXV6lTp5YkLVy4UP7+/sqYMWO826VLl876ccuWLdWzZ88E5wMAwJ5QbAEADqtFixaaOHGiIiIiVKxYMUVGRmrmzJk6dOiQYmJilD9//njlVZIyZ86sc+fOSZL27t1rvbxIkSJasGCB3nzzTUmPdk0fFx0drSpVqjyRwdvbWyNGjIh32bFjx/Txxx9rxYoVypIli/Xybdu2acSIEVq3bp3c3d2f+XnNnTtX3bt3t/754MGD2rdvnzZv3qzDhw/r+PHjmj9/vrJkyaIMGTJIkkJCQpQ/f/7nfr0AALBXFFsAgMNq3ry5cuTIoeHDh0uSMmbMqEKFCsnb21uFChWKVyrjtG/fXs2bN9f8+fOVO3du6+UTJ05U27Ztracu9+3bV1WrVrVe/yI7tp06ddKtW7fUsmVLSVKvXr1Uu3ZttW7dWunTp1edOnUkSWPGjFGpUqXUo0cPDRo0SNmyZdO1a9d06NCheDvGj59i3LZtW7Vv31558uTRpEmTNHPmTKVOnVqZM2fWjBkzEvaFAwDAzpgs/16qBgAgGRw4cEDFixfX/v375eXlZXQcvCK+nwAAI/HiUQAAAAAAu0axBQAAAADYNYotAAAAAMCu8eJRAABDHTt2zOgISAR8HwEARqLYAgAMkTVrVnl4eFhfGRj2z8PDQ1mzZjU6BgDACfGqyAAAw5w7d05Xr1415LEPHz6sLl26KG/evBo/frzSpk1rSI5XdefOHXXt2lVnzpzRpEmTVKRIEcOyZM2aVe+8845hjw8AcF4UWwCA09m9e7cCAgL03nvvaf369UqXLp3RkV7J7du3VbVqVf3+++/auHGjSpcubXQkAACSFS8eBQBwKrt27VKVKlX0/vvvO0SplaR06dJpw4YNeu+991S5cmXt2rXL6EgAACQrii0AwGns3LlTVapUUdGiRR2m1MZJly6d1q9fr6JFiyogIEA7d+40OhIAAMmGYgsAcAo///yzAgIC5OXlpXXr1tnt79Q+T7p06bRu3ToVK1ZMAQEB+vnnn42OBABAsqDYAgAc3vbt2xUQEKASJUo4bKmNkzZtWq1du1bFixdX1apVtWPHDqMjAQCQ5Ci2AACHtm3bNlWrVk2lSpXSmjVrlCZNGqMjJbm4cluiRAlVrVpV27dvNzoSAABJimILAHBYW7duVfXq1VW6dGmnKbVx0qRJo7Vr16pUqVKqVq2atm3bZnQkAACSDMUWAOCQwsLCVL16dZUpU0arV6+Wh4eH0ZGSnYeHh9asWaPSpUurWrVq2rp1q9GRAABIEhRbAIDDCQ0NVfXq1VW+fHmnLbVxPDw8tHr1apUrV07Vq1dXWFiY0ZEAAEh0FFsAgEMJCQlRjRo1VKFCBa1cuVKpU6c2OpLhPDw8tGrVKpUvX17Vq1dXSEiI0ZEAAEhUFFsAgMPYvHmzatSooYoVK2rFihWU2sekTp1aK1euVIUKFVSzZk1t2bLF6EgAACQaii0AwCFs2rRJtWrVktlsptQ+Q1y5rVSpkmrWrKnNmzcbHQkAgERBsQUA2L2NGzeqdu3a8vX11fLly+Xu7m50JJvl7u6u5cuXy8fHR7Vq1dKmTZuMjgQAwCuj2AIA7FpwcLBq164tPz8/LVu2jFKbAO7u7lq2bJl8fX1Vq1YtBQcHGx0JAIBXQrEFANitDRs2qE6dOqpcubKWLl0qNzc3oyPZjbhy6+/vrzp16mjDhg1GRwIA4KVRbAEAdmndunWqU6eOqlSpoiVLllBqX4Kbm5uWLl2qKlWqqG7dulq/fr3RkQAAeCkUWwCA3Vm7dq3q1aunatWqUWpfkZubmxYvXqyAgADVrVtX69atMzoSAAAvjGILALAra9asUb169VS9enUtWrRIqVKlMjqS3Ysrt9WqVVO9evW0Zs0aoyMBAPBCKLYAALuxevVq1a9fXzVr1qTUJrJUqVJp0aJFqlGjhurXr6/Vq1cbHQkAgASj2AIA7MLKlSvVoEED1a5dWz/99JNSpkxpdCSHkypVKv3000+qVauWGjRooFWrVhkdCQCABKHYAgBs3ooVK9SwYUPVqVNHCxYsoNQmoZQpU2rhwoWqXbu2GjZsqJUrVxodCQCA/0SxBQDYtOXLl6tRo0aqV6+e5s+fT6lNBilTptSCBQtUt25dNWzYUMuXLzc6EgAAz0WxBQDYrKVLl6px48Zq0KABpTaZpUyZUvPmzVP9+vXVuHFjLVu2zOhIAAA8E8UWAGCTlixZoiZNmqhhw4YKCgpSihQpjI7kdOLKbcOGDdW4cWMtXbrU6EgAADwVxRYAYHMWL16spk2bqkmTJpo7dy6l1kApUqTQ3Llz1bhxYzVp0kSLFy82OhIAAE/gJwUAgE356aef1KJFCzVt2lSzZs2i1NqAFClSaM6cOTKZTGrWrJksFosaN25sdCwAAKz4aQEAYDMWLlyoFi1aqEWLFpo5c6ZcXV2NjoT/L67curi4qHnz5rJYLGrSpInRsQAAkESxBQDYiPnz56tVq1Zq2bKlZsyYQam1Qa6urpo1a5ZMJpOaN2+u2NhYNWvWzOhYAABQbAEAxps3b55at26tVq1aafr06ZRaG+bq6qqZM2fKZDKpZcuWslgsat68udGxAABOjmILADBUUFCQ2rRpozZt2ujHH3+k1NoBV1dXzZgxQy4uLmrVqpViY2PVsmVLo2MBAJwYxRYAYJg5c+aobdu2+vDDD/Xjjz/KxYUX67cXrq6umj59ukwmk9q0aSOLxaJWrVoZHQsA4KQotgAAQ8yePVsffvihPvroI02ZMoVSa4dcXFw0bdo0ubi4qE2bNoqNjVWbNm2MjgUAcEIUWwBAsps5c6Y++ugjtW/fXj/88AOl1o65uLho6tSpMplM+vDDD2WxWNS2bVujYwEAnAzFFgCQrGbMmKH27dvr448/1qRJkyi1DsDFxcW6696uXTtZLBZ9+OGHRscCADgRii0AINlMmzZNHTp00CeffKKJEydSah2Ii4uLJk+eLJPJpI8++kixsbH66KOPjI4FAHASFFsAQLL48ccf9fHHH6tz586aMGGCTCaT0ZGQyFxcXKy78O3bt5fFYlH79u2NjgUAcAIUWwBAkps6dao6duyoLl26aPz48ZRaB+bi4qKJEyfKZDKpQ4cOio2N1ccff2x0LACAg6PYAgCS1A8//KBOnTqpW7du+u677yi1TsBkMmnChAlycXFRx44dZbFY1LFjR6NjAQAcGMUWAJBkJk2apC5duujTTz/VuHHjKLVOxGQy6fvvv5eLi4s++eQTxcbGqlOnTkbHAgA4KIotACBJTJw4UV27dlWPHj00duxYSq0TMplM+vbbb2UymdS5c2dZLBZ17tzZ6FgAAAdEsQUAJLrx48ere/fu6tmzp7755htKrRMzmUzW3fouXbooNjZWXbt2NToWAMDBUGwBAInq+++/16effqrPPvtMX331FaUWMplMGjt2rFxcXNStWzdZLBZ169bN6FgAAAdCsQUAJJpvv/1WPXv2VJ8+fTR69GhKLaxMJpO+/vprubi4qHv37oqNjdWnn35qdCwAgIOg2AIAEsW4cePUq1cv9e3bV6NGjaLU4gkmk0ljxoyRyWRSjx49FBsbq549exodCwDgACi2AIBX9s0336h3797q37+/Ro4cSanFM5lMJo0ePVouLi7q1auXLBaLevXqZXQsAICdo9gCAF7J119/rT59+mjgwIEaMWIEpRb/yWQy6csvv5SLi4s+++wzWSwWffbZZ0bHAgDYMYotAOCljRkzRv369dOgQYP0v//9j1KLBDOZTPriiy9kMpnUu3dvxcbGqk+fPkbHAgDYKYotAOCljB49Wv3799eQIUP0+eefU2rxwkwmk0aMGCEXFxf17dtXFotFffv2NToWAMAOUWwBAC/syy+/1MCBAzVs2DAFBgYaHQd2zGQyafjw4XJxcVG/fv0UGxur/v37Gx0LAGBnKLYAgBfyxRdfaPDgwQoMDNSwYcOMjgMHYDKZrLv+AwYMUGxsrAYOHGh0LACAHaHYAgASbMSIEdZTj4cOHWp0HDiYwMBAmUwmDRo0SBaLRYMGDTI6EgDATlBsAQAJMnz4cA0bNkzDhw/XkCFDjI4DBzVs2DCZTCYNHjxYsbGx/F0DACQIxRYA8J8CAwP1+eefa8SIEeyiIckNHTpULi4uGjJkiGJjYznlHQDwnyi2AIBnslgsCgwM1PDhwzVy5EgNGDDA6EhwEoMHD5aLi4v1tGRepAwA8DwUWwDAU1ksFg0bNkz/+9//9OWXX/JKtUh2AwcOlMlk0sCBA63llreVAgA8DcUWAPAEi8WiIUOG6IsvvtDo0aN5b1EYZsCAAXJxcVH//v0VGxur4cOHU24BAE+g2AIA4rFYLBo8eLBGjhypr776Sr179zY6Epxcv3795OLior59+8piseh///sf5RYAEA/FFgBgZbFYNHDgQI0aNUpff/21PvvsM6MjAZKkPn36yGQyqU+fPoqNjdUXX3xBuQUAWFFsAQCSHpXa/v37a8yYMRo7dqx69uxpdCQgnt69e8vFxUWfffaZYmNj9eWXX1JuAQCSKLYAAD0qtf369dNXX32lcePGqUePHkZHAp6qV69eMplM6tWrlywWi0aNGkW5BQBQbAHA2VksFvXp00fffPONvvvuO3Xv3t3oSMBz9ezZUy4uLurRo4diY2M1ZswYyi0AODmKLQA4MYvFos8++0zjxo3T+PHj1bVrV6MjAQny6aefysXFRd27d1dsbKy+/vpryi0AODGKLQA4KYvFol69eunbb7/VhAkT1KVLF6MjAS+kW7duMplM6tatmywWi7755hvKLQA4KYotADghi8WiHj166Pvvv9fEiRPVuXNnoyMBL6Vr164ymUzq2rWrYmNjNW7cOMotADghii0AOBmLxaJPP/1U48eP1+TJk/XJJ58YHQl4JV26dJGLi4s6d+4si8Wib7/9lnILAE6GYgsATsRisahbt26aOHGipkyZoo8//tjoSECi6NSpk1xcXPTJJ58oNjZW33//PeUWAJwIxRYAnERsbKy6du2qyZMna+rUqerQoYPRkYBE1bFjR5lMJnXs2FGxsbGaMGEC5RYAnATFFgCcQGxsrLp06aIpU6Zo2rRp+uijj4yOBCSJjz/+WC4uLurQoYMsFosmTJggFxcXo2MBAJIYxRYAHFxsbKw6d+6sqVOnatq0aWrXrp3RkYAk1b59e5lMJmu5nThxIuUWABwcxRYAHFhsbKw++eQTTZs2TdOnT9eHH35odCQgWXz00UcymUxq3769YmNjNXnyZMotADgwii0AOKjY2Fh17NhR06dP18yZM9WmTRujIwHJql27dnJxcVG7du0UGxurKVOmUG4BwEFRbAHAAcXGxqpDhw6aOXOmZs2apdatWxsdCTBE27ZtZTKZ9OGHH8pisWjq1KmUWwBwQBRbAHAwDx8+VPv27TVnzhzNmTNHLVu2NDoSYKg2bdrIxcVFbdq0UWxsrKZNm0a5BQAHQ7EFAAfy8OFDffTRR5o7d67mzJmjFi1aGB0JsAmtWrWSyWRSmzZtZLFYNG3aNLm6uhodCwCQSCi2AOAgHj58qHbt2ikoKEhz585V8+bNjY4E2JSWLVvKxcVFrVq1ksVi0fTp0ym3AOAgKLYA4AAePnyotm3bav78+Zo3b56aNm1qdCTAJjVv3lwmk0ktW7ZUbGysZs6cSbkFAAdAsQUAO/fw4UO1adNGCxcu1Pz589WkSROjIwE2rVmzZnJxcVGLFi1ksVg0a9Ysyi0A2DmKLQDYsQcPHqhNmzb66aeftGDBAjVq1MjoSIBdaNKkiUwmk5o3b67Y2FjNnj1bKVLwYxEA2Cv+BQcAO/XgwQO1atVKixcv1sKFC9WwYUOjIwF2pXHjxjKZTGrWrJksFovmzJlDuQUAO8W/3gBghx48eKCWLVtq6dKl+umnn9SgQQOjIwF2qVGjRnJxcVHTpk1lsVg0d+5cyi0A2CGTxWKxGB0CAJBwMTExatGihZYvX65FixapXr16RkcC7N6yZcvUpEkT1a9fX/PmzaPcAoCdodgCgB2JiYlR8+bNtWLFCi1evFh169Y1OhLgMJYvX67GjRurXr16mjdvnlKmTGl0JABAAlFsAcBOxMTEqFmzZlq1apUWL16sOnXqGB0JcDgrV65Uo0aNVLt2bS1YsIByCwB2gmILAHYgOjpaTZs21Zo1a7R06VLVqlXL6EiAw1q1apUaNmyoWrVqaeHChZRbALADFFsAsHHR0dFq0qSJ1q1bp6VLl6pmzZpGRwIc3urVq9WgQQPVrFlTCxcuVKpUqYyOBAB4DootANiw6OhoNW7cWOvXr9eyZctUo0YNoyMBTmPNmjVq0KCBqlWrpkWLFlFuAcCGUWwBwEbdv39fjRo1UnBwsJYvX67q1asbHQlwOuvWrVO9evVUtWpVLV68mHILADaKYgsANuj+/ftq2LChNm3apBUrVqhq1apGRwKc1vr161WvXj1VqVJFixcvlpubm9GRAAD/QrEFABtz//59NWjQQJs3b9bKlSsVEBBgdCTA6W3YsEF169aVv7+/li5dSrkFABvjYnQAAMD/iYqKUv369bVlyxatWrWKUgvYiKpVq2rlypXavHmz6tevr6ioKKMjAQAew44tANiIqKgo1atXT2FhYVq1apUqV65sdCQA/7Jx40bVqVNHPj4+WrZsmdzd3Y2OBAAQxRYAbEJUVJTq1q2rbdu2afXq1fLz8zM6EoBn2Lx5s2rVqiWz2azly5dTbgHABlBsAcBg9+7dU926dbV9+3atWbNGvr6+RkcC8B+2bNmiWrVqqUKFClqxYoVSp05tdCQAcGoUWwAw0N27d1WnTh39/PPPWrt2rXx8fIyOBCCBQkJCVLNmTXl7e2vlypWUWwAwEMUWAAxy9+5d1a5dW7t27dLatWtlNpuNjgTgBYWFhalGjRoqV66cVq5cKQ8PD6MjAYBTotgCgAHu3r2rWrVqaffu3Vq3bp0qVapkdCQAL2nr1q2qXr26ypYtq1WrVlFuAcAAvN0PACSzf/75RzVr1tQvv/yi9evXU2oBO1epUiWtX79eu3fvVs2aNfXPP/8YHQkAnA47tgCQjOJK7b59+7R+/Xp5e3sbHQlAItm+fbuqVaumkiVLas2aNUqTJo3RkQDAaVBsASCZ3LlzRzVq1NCBAwe0YcMGlS9f3uhIABLZjh07VK1aNRUvXlxr166l3AJAMqHYAkAyuHPnjqpXr66DBw9qw4YNKleunNGRACSRn3/+WVWrVpWXl5fWrl2rtGnTGh0JABwexRYAktjt27dVvXp1/fbbbwoODlbZsmWNjgQgie3cuVNVq1ZV0aJFtW7dOsotACQxii0AJKHbt2+rWrVqOnz4sIKDg1WmTBmjIwFIJrt27VJAQIA++OADrVu3TunSpTM6EgA4LIotACSRW7duqVq1avr999+1ceNGlS5d2uhIAJLZ7t27FRAQoCJFimj9+vVKnz690ZEAwCFRbAEgCdy6dUtVq1bV0aNHtXHjRpUqVcroSAAMsmfPHlWpUkWFChXShg0bKLcAkAQotgCQyG7evKmqVavq2LFj2rRpk0qWLGl0JAAG27t3rypXrqyCBQtqw4YNypAhg9GRAMChUGwBIBHduHFDAQEBOnHihDZt2qQSJUoYHQmAjdi3b58qV66s/PnzKzg4mHILAImIYgsAieTGjRuqUqWKTp06pU2bNql48eJGRwJgY/bv3y9/f3/ly5dPwcHBypgxo9GRAMAhUGwBIBFERkaqSpUqOn36tDZv3iwvLy+jIwGwUQcOHJC/v788PT21ceNGyi0AJAIXowMAgL2LjIxU5cqVdebMGW3ZsoVSC+C5vLy8tGXLFp0+fVqVK1dWZGSk0ZEAwO6xYwsAr+D69euqXLmyIiIitGXLFhUtWtToSADsxMGDB+Xn56fcuXNr06ZNypQpk9GRAMBusWMLAC/p+vXr8vf31x9//KGQkBBKLYAXUrRoUYWEhCgiIkL+/v66fv260ZEAwG5RbAHgJVy7dk1+fn76888/FRISog8++MDoSADs0AcffKCQkBCdO3dOfn5+unbtmtGRAMAucSoyALygq1evyt/fXxcuXFBISIjee+89oyMBsHOHDx+Wr6+vsmfPrs2bNytLlixGRwIAu8KOLQC8gKtXr8rPz08XL15UaGgopRZAonjvvfcUGhqqCxcuyM/PT1evXjU6EgDYFYotACTQlStX5Ovrq0uXLik0NFRFihQxOhIAB1KkSBGFhobqr7/+kq+vr65cuWJ0JACwGxRbAEiAy5cvy9fXV5cvX1ZoaKgKFy5sdCQADqhw4cIKDQ3V33//LT8/P8otACQQxRYA/kNcqb1y5YpCQ0NVqFAhoyMBcGCFChVSaGhovAU1AMDz8eJRAPAcf//9t3x9fXX9+nWFhoaqQIECRkcC4CSOHz8uHx8fZc6cWSEhIXr99deNjgQANosdWwB4hkuXLsnHx0eRkZEKCwuj1AJIVgUKFFBoaKiuX78uX19f/f3330ZHAgCbRbEFgKf466+/5OPjo5s3byosLEz58+c3OhIAJ1SgQAGFhYUpMjJSPj4+unTpktGRAMAmUWwB4F/iSu3t27cVFhamfPnyGR0JgBPLnz+/wsLCdPPmTfn4+Oivv/4yOhIA2Bx+xxYAHnPx4kX5+Pjon3/+UVhYmDw9PY2OBACSpJMnT8rHx0dp06ZVaGio3nzzTaMjAYDNYMcWAP6/CxcuyGw26+7du5RaADbn3XffVVhYmP755x+ZzWZdvHjR6EgAYDMotgAg6fz58zKbzYqKiqLUArBZnp6eCgsL071792Q2m3XhwgWjIwGATeBUZABOL67UxsTEKDQ0VHny5DE6EgA815kzZ2Q2m+Xm5qbQ0FBlz57d6EgAYCh2bAE4tT///FNms1kPHjxQWFgYpRaAXciTJ4/CwsIUHR0ts9msP//80+hIAGAoii0Ap3Xu3Ll4pTZ37txGRwKABIsrtw8ePJDZbNa5c+eMjgQAhqHYAnBKf/zxh8xms2JjY7V161blypXL6EgA8MJy586tsLAwxcbGUm4BODWKLQCnExERIbPZLEkKCwtTzpw5jQ0EAK8gV65cCgsLk8Vikdls1h9//GF0JABIdhRbAE4lrtS6uLhQagE4jJw5c2rr1q2SJLPZrIiICGMDAUAyo9gCcBpnz55VpUqVlCJFCoWFhemdd94xOhIAJJp33nlHW7dulYuLi8xms86ePWt0JABINhRbAE7hzJkzqlSpklKmTKmwsDDlyJHD6EgAkOhy5MihsLAwpUiRQmazWWfOnDE6EgAkC4otAId3+vRpmc1mubu7a+vWrbzfIwCHFlduU6VKRbkF4DQotgAc2qlTp6ylNjQ0VG+//bbRkQAgyWXPnl1hYWFyd3dXpUqVdPr0aaMjAUCSotgCcFgnT56U2WyWh4eHwsLCKLUAnMrbb7+t0NBQeXh4qFKlSjp16pTRkQAgyVBsATikuFKbNm1ahYWF6a233jI6EgAku7hymzZtWpnNZp08edLoSACQJCi2ABxOeHi4KlWqpPTp0yssLExvvvmm0ZEAwDBvvfWWQkNDlS5dOpnNZp04ccLoSACQ6Ci2ABxKeHi4fHx8lDFjRoWGhuqNN94wOhIAGO7NN99UaGio0qdPL7PZrPDwcKMjAUCiotgCcBjHjx+X2WxWpkyZKLUA8C9vvPGGwsLClClTJvn4+Oj48eNGRwKAREOxBeAQjh07JrPZrCxZsig0NFSvv/660ZEAwOa8/vrrCgkJsZbbY8eOGR0JABIFxRaA3Tt69KjMZrOyZcumkJAQvfbaa0ZHAgCb9frrrys0NFRZsmSRj4+Pjh49anQkAHhlFFsAdu3IkSPy8fGx7kJQagHgv7322msKDQ3Va6+9RrkF4BAotgDs1u+//y4fHx+98cYbCgkJUbZs2YyOBAB2I1u2bNqyZYtef/11mc1m/f7770ZHAoCXRrEFYJcOHz4sHx8fvfXWW9qyZYuyZs1qdCQAsDtxv8Lx5ptvytfXl3ILwG5RbAHYnUOHDsnX11fZs2en1ALAK8qaNatCQkL09ttvy8fHR4cPHzY6EgC8MIotALvy22+/ydfXVzly5NCWLVuUJUsWoyMBgN3LkiWLNm/erBw5csjHx0e//fab0ZEA4IVQbAHYjYMHD8rX11c5c+bU5s2blTlzZqMjAYDDiCu377zzjvz8/Ci3AOwKxRaAXfj111/l5+en3LlzU2oBIIlkzpxZmzdvVq5cueTr66uDBw8aHQkAEoRiC8DmHThwQH5+fsqTJ482b96sTJkyGR0JABxW5syZtWnTJuXJk0e+vr46cOCA0ZEA4D9RbAHYtP3798vPz0+enp7atGmTMmbMaHQkAHB4mTJl0qZNm+Tp6Sl/f3/KLQCbR7EFYLP27dsnf39/5c+fn1ILAMksY8aM2rhxo9599135+flp//79RkcCgGei2AKwSXv37pW/v78KFCig4OBgZciQwehIAOB04spt/vz55e/vr7179xodCQCeimILwObs2bNH/v7+KlSoEKUWAAyWIUMGBQcHq0CBAqpcubL27NljdCQAeALFFoBN+eWXX1S5cmUVKVJEGzZsUPr06Y2OBABOL67cFipUSJUrV9Yvv/xidCQAiIdiC8Bm7Nq1S5UrV9Z7771HqQUAG5M+fXoFBwerSJEiqlKlinbv3m10JACwotgCsAk7d+5UQECAPvjgA61fv17p0qUzOhIA4F/SpUunDRs26L333lOVKlW0a9cuoyMBgCSKLQAb8PPPPysgIEBFixal1AKAjUuXLp3Wr1+vDz74QAEBAdq5c6fRkQCAYgvAWDt27FDVqlXl5eWldevWKW3atEZHAgD8h7hyW7RoUQUEBOjnn382OhIAJ0exBWCY7du3q2rVqipRogSlFgDsTNq0abVu3ToVL15cAQEB2rFjh9GRADgxii0AQ2zbtk3VqlVTqVKltGbNGqVJk8boSACAF5Q2bVqtXbtWJUuWVNWqVbV9+3ajIwFwUhRbAMlu69atqlatmkqXLk2pBQA7lyZNGq1Zs0alSpVStWrVtHXrVqMjAXBCFFsAySosLEzVq1dX2bJltXr1anl4eBgdCQDwiuLKbenSpVW9enWFhYUZHQmAk6HYAkg2ISEhql69usqVK0epBQAH4+HhodWrV6tcuXKqUaOGQkNDjY4EwIlQbAEkiy1btqhmzZry9vbWqlWrlDp1aqMjAQASmYeHh1atWqXy5curRo0aCgkJMToSACdBsQWQ5DZv3qyaNWuqYsWKWrlyJaUWABxY6tSptXLlSlWoUEE1atTQli1bjI4EwAlQbAEkqU2bNqlWrVoym81asWIFpRYAnEBcua1UqZJq1qypzZs3Gx0JgIOj2AJIMsHBwapVq5Z8fHy0fPlyubu7Gx0JAJBM3N3dtWLFCpnNZtWqVUsbN240OhIAB0axBZAkNmzYoDp16sjf359SCwBOyt3dXcuXL5evr69q166t4OBgoyMBcFAUWwCJbv369apbt64qV66spUuXys3NzehIAACDuLu7a9myZfL391edOnW0YcMGoyMBcEAUWwCJat26dapbt66qVKmiJUuWUGoBAHJzc9PSpUtVuXJl1alTR+vWrTM6EgAHQ7EFkGjWrFmjevXqqVq1apRaAEA8bm5uWrJkiapWrap69epp7dq1RkcC4EAotgASxerVq1W/fn1Vr15dixYtUqpUqYyOBACwMW5ublq8eLGqVaum+vXra82aNUZHAuAgKLYAXtmqVavUoEED1axZUz/99BOlFgDwTKlSpdKiRYtUvXp11a9fX6tXrzY6EgAHQLEF8MKioqK0b98+SdLKlSvVsGFD1a5dm1ILAEiQuHJbq1YtNWjQQCtXrpQk7du3T1FRUQanA2CPKLYAXtiXX36pBg0aaPny5WrYsKHq1KmjBQsWKGXKlEZHAwDYiZQpU2rhwoWqXbu2GjVqpBUrVqhBgwb68ssvjY4GwA6ZLBaLxegQAOyHxWJRgQIF9Pbbb2v79u2qV6+e5s2bR6kFALyUmJgYNW/eXCtWrFCFChV08eJFHTt2TCaTyehoAOwIO7YAXsihQ4d04sQJhYWFycvLS5K0bds2g1MBAOxV3Azx8vLS1q1bFR4ersOHDxucCoC9odgCeCFffPGF9eM9e/bo4sWLypw5s4GJAAD2LHPmzPrrr7+0Z88exZ1IOGLECINTAbA3FFsAL+TevXt66623NHbsWJ0/f147duxQsWLFjI4FALBTxYoV044dO3T+/HmNHTtWb731lu7du2d0LAB2ht+xBQAAAADYNXZsAQAAAAB2LYXRAYCkdO7cOV29etXoGEhEWbNm1TvvvGN0DABweMxQx8MMhSOj2MJhnTt3TgULFtTdu3eNjoJE5OHhoWPHjjGYASAJMUMdEzMUjoxiC4d19epV3b17V0FBQSpYsKDRcZAIjh07ppYtW+rq1asMZQBIQsxQx8MMhaOj2MLhFSxY0Pp+qwAAIOGYoQDsBS8eBbyEiIgI+fv7P/P6WbNm6datW8+9j7CwMB06dCixo8UzcOBA5cyZ86lZr127pkyZMikoKOiZx4eEhMhkMun8+fOSpPv376t169aqUKGC6tatq5s3b0qSAgMDVbBgQZnNZpnNZkVHRyfNJwQAQDKYOnWqdaYVKFBADRo0eOZtW7duHW/ONmjQQJUqVVKJEiU0btw46+WpU6e23ufUqVOTND/gjCi2QBJIimJ7586dF87RtWtXhYaGPvW6ESNGyNvb+5nHxsbG6uuvv1aJEiWsl82ePVu5cuXS9u3bVb16dY0dO9Z63aBBgxQWFqawsDClSpXqhbMCAPC4hw8fJsr9xMbGvvDvCn/88cfWmVaxYkU1adLkqbc7cODAE/N+wYIF2rp1q3bv3q1Jkybp9u3bkqS3337bep8ff/zxy30yAJ6JYgu8ohkzZqhkyZLy9fVVv379FBISooMHD6pRo0Zq3769IiMj5e/vL7PZrHLlyun48eO6fv26Zs2apS+++EJms/mZpfXOnTsKCgpSrVq11Lt37xfO9tZbb8nF5cmn+alTp3Tt2jUVL178mccGBQWpdu3aSpMmjfWy0NBQ1atXT5JUt27deKV5zJgx8vb2jrc6DQDAi4iIiFDJkiXVunVrtWzZUrNmzVL9+vXVsGFDFSxYUIsWLVLDhg1VtGhRff/998+9r0OHDqlv377y9vbWkSNHXipPVFSUNm7cqNq1az/1+uHDh2vQoEHxLotb3L17967eeecdeXh4SJIuXbqkSpUqqW7dujpz5sxL5QHwbPyOLfCKZs2apcWLFytXrlyKjY2Vi4uLihYtqqCgIGXPnl0xMTFat26dUqVKpfXr12v06NGaOXOm2rZtK09PT7Vs2fKJ+9y4caPmzJmjq1evqnbt2poxY4ayZcsmSdqzZ4/69u37xDFdu3ZVw4YNE5R56NCh+t///qe5c+c+9fp79+5pzpw52rBhgxYuXGi9PO70ZUnKlCmTrl27Jknq1q2bhg0bpvv376tOnToqWrSofHx8EpQFAIDHRUREaPPmzcqQIYNmzZql2NhYLVu2TDt27FCTJk108uRJubi4qEiRIurevXu8Y69cuaIZM2YoODhY+fPnV/PmzTV69GiZTCZJUufOnXX06NF4x6RNm1Zr1qx5apbVq1fL399f7u7uT1y3Zs0aeXl5Wefz4+rUqaNdu3apU6dOcnV1tX5eWbNm1ZYtW9SuXTuFhYW9zJcHwDNQbIFXNG7cOP3vf//TvXv31LRp0ydWdW/cuKEuXbro0qVLio6OVrp06f7zPufNm6fTp0+rW7duql27ttKmTWu9rlSpUq80DHfu3KksWbIob968z7zNt99+q08++UQpUsT/JyJLliyKjIxUrly5dOPGDWXJksV6uSS5u7urQYMG2rdvH8UWAPBSihQpogwZMlj/HHd2Ufbs2VWgQAHrDujThIeHa8aMGapVq5Zat26t999/P971kyZNeqEsc+bMeeoZUw8fPtTYsWO1evVqXbly5YnrV65cqX/++cd6GnOhQoWUNWtWSZKfn586duz4QjkA/DeKLfCKChYsqOnTpysqKkrvvvuuateurVSpUunBgweSHp3SW6xYMQ0YMEDr1q2z/l7q47f5t9mzZ+vatWv66aefVK9ePWXNmlVt27ZVQEDAK+/Y7tu3T4cOHVLVqlV16tQppUmTRnnz5lXZsmWttzly5Ii2bt2qadOm6dChQ2rVqpXWrFkjs9msVatWqVixYlq1apXMZrOkR+U9Y8aMslgsCg0NVbNmzV70ywgAgCRZdzjjxO22/vvjp/H29tbx48f1888/a/LkyTp69KiqVKmijh07KmvWrC+0Y3vlyhUdO3ZMFStWfOK6S5cuKTIyUg0aNNC9e/d05MgRDR8+XIMHD9bDhw+VMmVKpU6d2vrfnTt3lDp1arm6uur3339X5syZX+RLAiABKLbAK+rcubPOnj2r6OhoffLJJ5Kk+vXrq127dipevLjatm2r5s2ba/v27SpUqJD1uMqVK6tHjx5as2aN5syZ88QKdJYsWdS5c2fr/e/YsUPSi+3Yfvfdd1q8eLGOHTsmf39/TZo0Sd27d7eeuhUYGChPT0+VLVtWly5d0ldffaVvvvkm3islm81mzZ07V2nSpFHbtm3Vvn17VaxYUZkyZdLs2bMlST169FB4eLgsFosqVKjwzN9FAgAgqZlMJnl7e8vb21sxMTFav369/v77b2XNmvWFdmwXLlyoxo0bxyvTs2bN0ttvv63KlSvr119/lfToFOP27dtr6NChun37tmrVqiXp0TsJNG3aVLlz59aePXvUsWNH61lbU6ZMScTPGIAkmSwWi8XoEEBSOHDggIoXL679+/fzHnwOgu8pACQP/r11PHxP4eh4VWQAAAAAgF2j2AIAAAAA7BrFFgAAAABg1yi2gEECAwPjvUiTLduwYYPKly8vs9ksX19f/fnnn5IefQ4FCxaU2WyW2WxWdHT0E8du2bJFZcuWVdmyZbVgwQLr5f7+/sqWLZtGjBhhvezEiRPy8vJS2rRprS+WBQDAv9nTDI2MjFSVKlVUqVIllStXzvqiUwmZoeXKlVOlSpVUsmRJ6wyNjo5W48aNVaFCBZUqVUqbNm2SJJ05c0YVK1a03t8ff/yRfJ8kYAMotkAie/jwYbI/jsViUUJeB+5ls/n6+urnn39WWFiYWrZsqe+//9563aBBgxQWFqawsDClSpXqicfr1auX1qxZo5CQEI0ePVq3b9+W9OiVJb/66qt4t8+ePbu2bNmSoLctAgA4HkecofPmzVP58uW1detWjRkzJt6C7vNmqCSFhYVp69at2rJliwYOHChJ2rhxo9KkSaPt27dr6dKl6t+/v6RH79H70UcfKSwsTO3bt483qwFnQLGFU4mIiFD58uXl4+Mjs9msyMhI/f333/Lz81OVKlXUuXNn+fv7S5Latm1r3TWMGxLSo7fQ8fX1VYkSJTR48GDrfefMmVNdunRRlSpVdPPmTTVu3Fi+vr7y8fHRiRMnJEnLly9X0aJFVbt2be3fv/+ZOWNiYtShQwf5+vrK29tbu3btsmbq1KmTatWqpeDgYHl6emrQoEHy8/PT+fPn1bRpU1WqVEnly5e33v+/j3kZjw/bGzduxHvD+zFjxsjb21vjxo174rhTp04pV65cypIli1KnTq1y5cpp7969kh6V2H/z8PBQpkyZXiojACBpMUNfboYWLFhQt27dkiRdv35dr732mvW6581Q6f/m7+3bt1W4cGFJUt68eXX//n1ZLBZFRkZa769w4cK6cePGUx8HcAa8jy2cSkhIiCpXrqzAwEBJj1Zpe/bsqXbt2qlFixaaMWOGdYA+S/v27fXpp5/KYrGoXLly6tChg3LmzKmLFy+qT58+ypUrl/r376/69euradOmOnLkiPr27atly5apf//+2rNnj9KlSydfX99nPsb06dOVN29e/fjjj7py5Ypq165tHczZs2fX5MmTJT16D93q1avriy++0HfffacCBQpo4cKFCg8PV5s2bbR79+4njnnci7xR/fLly/XFF1/o5s2bWr9+vSSpW7duGjZsmO7fv686deqoaNGi8vHxsR5z7dq1eEU1U6ZMunbt2nO/vgAA28QMjS+hM9TLy0tDhgxRkSJFdOPGDW3btk3Sf89QSbp3754CAgJ05MgRjRo1SpKUJ08e3b17VwUKFNCNGze0cuVKSZKfn58CAgI0ffp0RUVFac+ePc/9XgCOhmILp9K4cWONHDlSLVq0UM6cORUYGKjw8HB169ZN0qPfZZk/f74kxXtD9sdPUVq6dKmmTZsmk8mks2fP6vz588qZM6eyZ8+uXLlySZIOHz6srVu36ocffrDe15UrV/Taa68pQ4YMkqQyZco8M+fhw4e1c+dObdiwQZKsK7CS5O3tbf3Y1dVV5cqVkyQdP35cDRo0kCTlz58/XoF8/JjHvcgb1derV0/16tXTwoULNXDgQC1atEhZsmSRJLm7u6tBgwbat29fvKGcJUsWRUZGWv9848YN6zEAAPvCDI0voTN0zJgxql+/vnr37q3du3erS5cuWr9+/X/OUElKnTq1tm3bpqtXr6pkyZJq3LixFi5cqOzZs2vFihWKiIhQvXr19Ouvv6pfv3763//+p4YNG2rhwoUaMGDAUws54KgotnAqLi4uGjlypCSpXbt2Cg4OVr58+fTLL78ob9681tVZScqcObPOnTsnSdbTZyVpyJAhOn78uNzc3FS+fHnrwHZ1dbXepnDhwipbtqzq1asn6dELPbi6uurvv//WrVu3lC5dOu3Zs0dFihR5as7ChQvL09NTPXv2tB4f5/HHMZlM1h8eChQooO3bt8vf31/h4eHxCuTjxzwuoavNUVFRcnd3l/Ro19XDw0PSox8WMmbMKIvFotDQUDVr1izecZ6enoqIiFBkZKQ8PDy0c+dOffnll0/NAgCwbczQ+F7krKds2bJZ/x9XtP9rhkZHRytFihRycXFRmjRp5O7ubp3FcfeXKVMm3blz57mPAzgLii2cypo1azR+/Hi5urrKzc1N3t7eKlGihJo3b66ZM2fGG5Lt27dX8+bNNX/+fOXOndt6ef369VW+fHkVKFBAadOmferjDBo0SJ988onGjx8vSQoICFC/fv305ZdfqmLFisqRI4cyZ878zJwdOnRQ165drSu3xYoV09ixY5/7uXXo0EFt27ZVxYoV9eDBA+tjP09CV5t//PFHLV26VCaTSW5ubpoyZYokqUePHgoPD5fFYlGFChVUu3Zt6+WDBg1StmzZ9PXXX6t69eqSpN69eyt9+vSSHv1Q9Msvv+j+/fv65ZdftHr1akVGRqpRo0Y6evSojhw5ooCAgHgvsgEAMA4zNL6EztBu3bqpVatWmjlzpu7du6fRo0dL+u8ZevPmTbVr106urq66f/++hg0bJjc3N7Vs2VLNmjVTpUqVdPfuXX3xxReSpMGDB6tjx45KkSKFYmJirLMacBYmS0JeBg6wQwcOHFDx4sW1f/9+eXl5JeiYiIgItW/fXps3b07idHgZL/M9BQC8OGao42GGwtGxYwsYrHXr1tbTtaRHp28tW7bMwEQAANgHZiiAOBRb4DG5cuVK9pXmOXPmJOvjAQCQFJihAIzE+9gCdsDT0zNZHmfKlCkqVaqUKlasqGbNmun+/fuSpH379qlMmTKqVKmSqlWrpps3b8Y7rmLFitb3KAQAwFYl1zwdPny4zGazzGazcuXKpc8++0yS9Oeff8rf318VKlRQ7969kyUL4CwotgCs/Pz8tHv3bm3btk05cuRQUFCQJGnUqFEaPXq0tm7dqvLly8dbIV++fLkyZsxoUGIAAGzP0KFDFRYWprCwMOXLl09NmjSRJPXr108DBw7U9u3bdfHiRYWGhhqcFHAcFFvgFURERKh8+fLy8fGR2WxWZGSktm3bJh8fH+srHN67d0+SZDab1bNnTwUEBMhsNmv+/PkKCAhQ0aJFrW9obzab1aVLF1WuXFl+fn66fPlyvMeLiYlRhw4d5OvrK29vb+sbzo8YMUKlSpWSr6/vf77y4/N4enrKxeXRPwtubm7WtzgoXLiw9W0DIiMj9dprr0mSHjx4oMmTJ6tLly4v/ZgAADjaPI1z8eJFnT9/XqVKlZL06AWcfH19JUl169al2AKJiGILvIKQkBBVrlxZoaGhCgsLU8aMGVW8eHGFhoZq+/btKliwoBYtWmS9faVKlRQcHCxPT0/t3btXwcHB6t27t2bMmGG9TenSpbVp0ya1aNFCX331VbzHmz59uvLmzauQkBAtX75cvXr1kiTNmzdPoaGhCgkJUY8ePZ7IWb9+fespUXH/Pe/U4WPHjmn9+vVq1KiRJKlevXrq1q2bihQpop07d6p+/fqSpB9++EEtW7aUm5vbS38NAQBw1Hk6b948NW/e3Prn2NhY68eZMmXStWvXXvhrBeDpePEo4BU0btxYI0eOVIsWLZQzZ04FBgbqyJEjGjx4sO7fv6+///7b+r6tklS8eHFJUvbs2ZU3b17rx1u3brXeply5ctb/L1++PN7jHT58WDt37tSGDRskybqLOmHCBHXp0kUPHjzQJ598Im9v73jHvcgrREZERKhNmzZatGiR0qRJI0nq1KmTli1bphIlSmjUqFEaO3asOnXqpBUrVmjjxo3atm1bgu8fAIB/c8R5KklBQUFauXKl9c9xZ0XFPWaWLFle6P4APBvFFngFLi4uGjlypCSpXbt2Cg4O1rRp0/T555+rbNmy6tu3rx5/q2iTyfTUjx+/ze7du+Xp6andu3crf/788R6vcOHC8vT0VM+ePSVJ0dHRkqSyZcvKz89P586dU7169bR///54x9WvX1/Xr1+Pd5mnp6emTZsW77JLly6pUaNGmj59uvLkyRPvumzZsln/f+rUKR07dky3bt1S9erVdf36df3111+aMmWKOnbsmICvHAAA/8fR5qkkHTx4UBkyZFCuXLmslxUrVkxbt25VpUqVtGrVKn344YcJ+voA+G8UW+AVrFmzRuPHj5erq6vc3Nzk7e2tO3fu6KOPPlKBAgWUPn36eCvMCXHgwAHNnj1bDx8+1IIFC+Jd16FDB3Xt2lU+Pj6SHg3IsWPHql69eoqKilJUVNRTf981oSvM/fv3199//63u3btLkpo3b66PP/5Yo0aNUpMmTeTu7i4XFxcFBQXprbfe0p49eyRJYWFhCgoKotQCAF6Ko81TSZo7d65atmwZ77JRo0apXbt2io6OVokSJayPD+DVmSyPL20BDuTAgQMqXry49u/fLy8vL6PjJIjZbFZQUJCyZ89udBSbZI/fUwCwR/b+7y3z9En2/j0F/gsvHgUAAAAAsGucigzYkLCwMKMjAABg95ingPNhxxYAAAAAYNcotkAyiIiIkL+/f7I93pIlS1SwYEG5u7vHu3zVqlUqXbq0KlSooIULFz712JCQEPn7+8vHx0cDBgyQJJ04cUJeXl5KmzatduzY8cQxQ4cOlaenZ+J/IgAAp5fcM/SLL75QxYoVVb58ebVu3VoxMTGSHv3ebtmyZWU2m9W5c+dnHh8eHq6UKVNa52VYWJjefPNN6/vexr3w4rNmNYCXw6nIgAMym8369ddfVaRIEetlsbGx6tOnj/bt2yd3d3eVL19eNWrUULp06ay3uXbtmr755hutXbtWbm5u1suzZ8+uLVu2WN8W4XEXLlzQyZMnk/YTAgAgmfTp00eDBg2SJLVu3VobN25UjRo1JEmLFy/+zxekCgwMlNlsjndZjRo1nnhLoKfNagAvjx1b4CX17t1bS5culSQ9ePBA77//vmJiYjRw4ED5+vrKy8tLkydPfuK4tm3bxlvFbd++vSTp999/l7+/v3x9fdWoUSPdvXv3pbNlzZr1iRXgq1evKlu2bEqXLp1SpkypPHnyaO/evfFus3btWmXLlk1169aVv7+/du/eLUny8PBQpkyZnvpYgYGBGjx48EtnBQA4H1ueoalSpZL0aEH4wYMH1jOSTCaTmjZtKh8fH23evPmpx27btk25cuXS22+/He/yjRs3ytvbW507d7Zme9qsBvDyKLbAS2rbtq1mz54tSQoODpavr69SpkypQYMGKSQkRLt27dK4ceOspzD9l86dO2vGjBkKCQmR2WzW1KlT410fHR1tPY3p8f8SWiqzZs2qK1eu6MKFC7p586Z27dr1xJvMX7hwQceOHdPy5cs1ffp0tWvX7rn3eejQIUmP3ugeAICEsvUZOmzYMOXLl083btxQjhw5JD3ard2xY4eCgoLUtWtX3bx5M94xFotFI0eOVL9+/eJdXrx4cZ04cUI7duxQ1qxZNXr06AR9TgBeDKciAy+pSJEiunLlii5fvqzZs2dbfx918uTJWrFihVxdXXX58mVdvnw53nEmk8n68eNvI33kyBG1bt1aknT//v0nTmNKlSrVK73Ko4uLi6ZMmaKWLVsqXbp0Klq0qN566614t8mcObN8fHzk7u6unDlzKn369Lp+/boyZ8781PscOnSoJkyY8NKZAADOydZn6Oeff67AwEB16dJFs2bNUufOnZU1a1ZJ0ttvvy0vLy+dOHFCJUuWtB6zcOFC+fv7K2PGjPHu6/Ff+WnZsuVTf60HwKuj2AKvoEWLFpo4caIiIiJUrFgxRUZGaubMmTp06JBiYmKUP3/+eINXelQez507J0nxTgUuUqSIFixYoDfffFPSo9Xlx0VHR6tKlSpPZPD29taIESMSlDduhfr27dtq0KBBvIEsST4+Plq6dKksFotu3rypyMjIZ56CLEmnT5+2ngb2119/qUuXLpo4cWKCsgAAnJutztCoqCi5u7vLZDIpQ4YM8vDwkMVi0e3bt5U+fXrduXNHv/32m3LlyhXvuIMHD2rfvn3avHmzDh8+rOPHj2v+/PnKkiWLMmTIIOnRCzTmz5//5b5gAJ6LYgu8gubNmytHjhwaPny4JCljxowqVKiQvL29VahQIWXJkuWJY9q3b6/mzZtr/vz5yp07t/XyiRMnqm3bttbTrvr27auqVatar3+R1eawsDCNGDFCFy9elL+/vzp27KhGjRqpb9++2rt3r1KkSKFRo0YpZcqUkh79cDFv3jzly5dP1apVU6VKlXT//n2NHTtWJpNJkZGRatSokY4ePaojR44oICBAI0aM0OHDh62P6enpSakFACSYrc7Qzp0768yZM3r48KHy5cun4cOH68GDB/Lx8VHq1KkVHR2twYMHK1u2bJL+b4Y+fopx27Zt1b59e+XJk0eTJk3SzJkzlTp1amXOnFkzZsyQ9OxZDeDlmCz/XgoDHMSBAwdUvHhx7d+/X15eXkbHQSLgewoAyYN/bx0P31M4Ol48CgAAAABg1yi2AAAAAAC7RrEFAAAAANg1XjwKDu/YsWNGR0Ai4XsJAMmLf3cdB99LODqKLRxW1qxZ5eHhoZYtWxodBYnIw8PD+l6CAICkwQx1TMxQODJeFRkO7dy5c7p69arRMRLswYMHatiwoXLlyqVvv/02yR6nR48e+uOPP7R48WKlSGFf61tZs2bVO++8Y3QMAHB4zNCnY4YCtoliC9iQOXPmqE2bNtq3b5+KFy+eZI+zb98+lSxZUnPmzFGrVq2S7HEAAEguzFDAuVFsARvx4MEDFSxYUIUKFdLKlSuT/PFq166t48eP6+jRo3a34gwAwOOYoQB4VWTARsybN0+nTp1SYGBgsjxeYGCgTp48qfnz5yfL4wEAkFSYoQDYsQVswIMHD1SgQAG99957Wr58ebI9bt26dXXkyBEdO3aMFWcAgF1ihgKQ2LEFbMLcuXN1+vTpZFtpjhMYGKhTp04pKCgoWR8XAIDEwgwFILFjCxguJiZG+fPnV7FixbR06dJkf/z69evrt99+0/Hjx5UyZcpkf3wAAF4WMxRAHHZsAYPNmTNHZ8+e1bBhwwx5/GHDhunMmTOaO3euIY8PAMDLYoYCiMOOLWCgmJgY5cuXT8WLF9eSJUsMy9GgQQP9+uuvCg8PZ8UZAGAXmKEAHseOLWCg2bNnKyIiwrCV5jjDhg3T2bNnNWfOHENzAACQUMxQAI9jxxYwSHR0tPLly6dSpUpp0aJFRsdRo0aNtG/fPoWHhytVqlRGxwEA4JmYoQD+jR1bwCCzZs3SuXPnDF9pjjNs2DD98ccfmj17ttFRAAB4LmYogH9jxxYwQHR0tN59912VLVtWCxcuNDqOVZMmTbR7926dPHmSFWcAgE1ihgJ4GnZsAQPMmDFDf/75p4YOHWp0lHiGDh2qP//8U7NmzTI6CgAATzVz5kxmKIAnsGMLJLP79+/r3XffVfny5bVgwQKj4zyhadOm2rVrFyvOAACbwwwF8Czs2ALJbMaMGTp//rzNrTTHiVtxnjFjhtFRAACIhxkK4FnYsQWS0f379+Xp6amKFStq3rx5Rsd5pubNm2v79u06deqU3NzcjI4DAAAzFMBzsWMLJKNp06bp4sWLNrvSHGfo0KG6ePGipk+fbnQUAAAkMUMBPB87tkAyiYqKUt68eeXr66u5c+caHec/tWzZUmFhYTp16pTc3d2NjgMAcGLMUAD/hR1bIJn8+OOPunTpkoYMGWJ0lAQZMmSI/vrrL1acAQCGmzZtGjMUwHOxYwskg6ioKOXJk0f+/v6aM2eO0XESrFWrVgoJCdHp06dZcQYAGCJut9bPz48ZCuCZ2LEFksHUqVN1+fJlu1lpjjNkyBBdunRJP/74o9FRAABOaurUqfr777+ZoQCeix1bIIndu3dPefLkUUBAgF2+aXubNm20adMmnT59WqlTpzY6DgDAiTBDASQUO7ZAEpsyZYquXLlidyvNcYYMGaLLly9r6tSpRkcBADgZZiiAhGLHFkhCd+/eVZ48eVS9enW7frP2Dz/8UBs2bNCZM2dYcQYAJAtmKIAXwY4tkIR++OEHXb16VYMGDTI6yisZPHiwrly5oilTphgdBQDgJKZMmaKrV69q8ODBRkd5JcxQIHmwYwskkbt37yp37tyqWbOmQ7zcf7t27bRu3TqdOXNGHh4eRscBADiwuN3aGjVqMEMBJAg7tkASmTx5sq5fv273K81xBg8erGvXrumHH34wOgoAwMFNnjxZ165dY4YCSDB2bIEk8M8//yh37tyqU6eOQ73Mf/v27bV69WqdOXNGadKkMToOAMABMUMBvAx2bIEkMGnSJEVGRtr979b+2+DBg3X9+nVNnjzZ6CgAAAfFDAXwMtixBRLZnTt3lDt3btWvX98hXyji448/1ooVK3T27FlWnAEAiYoZCuBlsWMLJLKJEyfq5s2bGjhwoNFRksSgQYMUGRmpSZMmGR0FAOBgJk2axAwF8FLYsQUS0Z07d5QrVy41bNjQoV8gomPHjlq2bJnOnj2rtGnTGh0HAOAA4nZrGzRowAwF8MLYsQUS0YQJE3Tr1i2HXWmOM2jQIN28eVMTJ040OgoAwEFMmDDBoXdr4zBDgaTBji2QSG7fvq1cuXKpSZMmTnGKUadOnbR48WKdPXtW6dKlMzoOAMCOMUMBvCp2bIFEMn78eN25c8fhV5rjDBw4ULdv39aECROMjgIAsHPMUACvih1bIBHcunVLuXLlUvPmzZ1qSHXp0kULFy5UREQEK84AgJdy69Yt5c6dW82aNWOGAnhp7NgCiWD8+PH6559/1L9/f6OjJKsBAwbozp07Gj9+vNFRAAB2Km63dsCAAUZHSVbMUCBxsWMLvKKbN28qd+7catGihVMOp65du2r+/PmKiIhQ+vTpjY4DALAjzFBmKJBY2LEFXtH333+vu3fvOt1Kc5wBAwbo7t27+v77742OAgCwM8xQZiiQWNixBV7BjRs3lDt3brVu3Vrfffed0XEM0717d82dO1cRERHKkCGD0XEAAHaAGfoIMxRIHOzYAq/gu+++U1RUlNP9bu2/9e/fX1FRUU79gwkA4MUwQx9hhgKJgx1b4CXduHFDuXLl0ocffqhx48YZHcdwPXr00OzZs3X27FllzJjR6DgAABvGDI2PGQq8OnZsgZf07bff6v79++rbt6/RUWxCv379WHEGACRI3Azt16+f0VFsAjMUeHUUW+AlREZGaty4cerUqZPefPNNo+PYhDfffFOffPKJxo0bpxs3bhgdBwBgox6foW+88YbRcWwCMxR4dRRb4CWMGzdOMTExrDT/S79+/RQdHc1pZQCAZ2KGPh0zFHg1FFvgBV2/fl3ffvutOnfurNdff93oODbljTfeUKdOnfTtt98qMjLS6DgAABvDDH02Zijwaii2wAsaO3asHj58yO/WPkPfvn0VExOjsWPHGh0FAGBjmKHPxwwFXh7FFngB165d03fffacuXbrotddeMzqOTXr99dfVpUsXfffdd7p+/brRcQAANuLatWv6/vvvmaHPwQwFXh7FFngBY8eOVWxsrHr37m10FJvWp08fPXz4kBVnAIBV3G5tnz59jI5i05ihwMuh2AIJdPXqVX3//ffq2rUrK83/4bXXXrOuOF+7ds3oOAAAgz0+Q7Nly2Z0HJvGDAVeDsUWSKBvvvlGFouFleYE6tOnjywWi7755hujowAADMYMfTHMUODFUWyBBLhy5YrGjx+vbt26KWvWrEbHsQvZsmVT165dNX78eF29etXoOAAAgzBDXxwzFHhxFFsgAb7++muZTCZ+t/YFxX29vv76a4OTAACMwgx9OcxQ4MVQbIH/cPnyZU2YMEHdu3dXlixZjI5jV7Jmzapu3bppwoQJunLlitFxAADJ7MqVK8zQl8QMBV4MxRb4D19//bVcXV3Vq1cvo6PYpc8++0wmk4kVZwBwQl999RUz9BUwQ4GEo9gCz3H58mVNnDiRleZXkCVLFnXv3l0TJkzQ5cuXjY4DAEgmzNBXxwwFEo5iCzzHmDFjlCJFClaaX9Fnn30mV1dXffXVV0ZHAQAkE2Zo4mCGAglDsQWe4dKlS5o0aZI+/fRTZc6c2eg4di1z5sz69NNPNXHiRP39999GxwEAJDFmaOJhhgIJQ7EFnmHMmDFKlSqVevbsaXQUh9CrVy+lTJmSFWcAcAJfffUVMzQRMUOB/0axBZ7ir7/+0uTJk9WjRw9lypTJ6DgOIVOmTOrRo4cmTZqkS5cuGR0HAJBELl26xAxNZMxQ4L9RbIGnGDNmjNzc3NSjRw+joziUnj17KlWqVBozZozRUQAASWT06NFKlSoVMzSRMUOB56PYAv/y119/6YcfflDPnj2VMWNGo+M4lIwZM6pHjx6aPHmy/vrrL6PjAAASGTM06TBDgeej2AL/MmrUKLm7u7PSnER69OghNzc3jR492ugoAIBExgxNWsxQ4NkotsBjLly4oClTpqhXr17KkCGD0XEcUsaMGdWrVy/98MMPunjxotFxAACJhBma9JihwLNRbIHHjBo1Sh4eHvr000+NjuLQPv30U6VOnZoVZwBwIKNHj2aGJgNmKPB0FFvg/7tw4YKmTp2qzz77TOnTpzc6jkPLkCGDPvvsM02ZMkUXLlwwOg4A4BUxQ5MPMxR4Ooot8P99+eWXSps2rbp162Z0FKfQvXt3eXh4aNSoUUZHAQC8oi+//FJp0qRhhiYTZijwJIotIOnPP//Ujz/+yEpzMkqfPr0+++wzTZ06VefPnzc6DgDgJTFDkx8zFHiSyWKxWIwOARitc+fOWrRokc6ePat06dIZHcdp3Lp1S7lz51bTpk01ceJEo+MAAF4CM9QYzFAgPnZs4fTOnTunadOmqXfv3gzkZJY+fXr17t1b06ZN059//ml0HADAC2KGGocZCsTHji2c3ieffKKlS5fq7NmzSps2rdFxnM7t27eVO3duNW7cWJMmTTI6DgDgBXTq1ElLlixhhhqEGQr8H3Zs4dT++OMPzZgxQ3369GEgGyRdunTq06ePpk2bpnPnzhkdBwCQQH/88YemT5/ODDUQMxT4P+zYwql17NhRy5YtY6XZYHfu3FHu3LnVoEED/fDDD0bHAQAkQNwMjYiIUJo0aYyO47SYocAj7NjCaUVERGjGjBnq27cvpdZgadOmVZ8+fTRjxgz98ccfRscBAPyHx2copdZYzFDgEXZs4bQ6dOigVatW6cyZMwxlG/DPP/8od+7cqlu3rqZOnWp0HADAczBDbQszFGDHFk7q7NmzmjVrFivNNiRNmjTq27evZs6cqYiICKPjAACegRlqe5ihADu2cFIfffSR1q5dqzNnzsjDw8PoOPj//vnnH+XJk0e1a9fWjz/+aHQcAMBTtG/fXmvWrGGG2hhmKJwdO7ZwOmfOnNHs2bPVr18/BrKNSZMmjfr166dZs2bpzJkzRscBAPwLM9R2MUPh7NixhdNp166d1q1bx0qzjbp7967y5MmjGjVqaPr06UbHAQA8pl27dlq/fr3OnDmj1KlTGx0H/8IMhTNjxxZO5dSpU5ozZ4769+9PqbVRHh4e6tevn2bPnq3Tp08bHQcA8P/FzdB+/fpRam0UMxTOjB1bOJW2bdtq48aNOn36NEPZht27d0958uRR1apVNXPmTKPjAADEDLUXzFA4K3Zs4TROnjypuXPnqn///gxkG5c6dWr1799fc+fO1alTp4yOAwBO79SpUwoKCmKG2gFmKJwVO7ZwGq1bt9aWLVt0+vRpubu7Gx0H/+HevXvKmzevqlSpolmzZhkdBwCcWps2bbR582ZmqJ1ghsIZsWMLp3DixAnNmzdPAwYMYCDbidSpU2vAgAGaO3euTp48aXQcAHBaJ06cUFBQEDPUjjBD4YzYsYVTaNWqlUJDQ3Xq1CmGsh2JiopS3rx55efnpzlz5hgdBwCcEjPUPjFD4WzYsYXDCw8P1/z581lptkPu7u4aMGCA5s2bp/DwcKPjAIDTYYbaL2YonA07tnB4LVq00LZt23Tq1Cm5ubkZHQcvKCoqSp6enjKbzQoKCjI6DgA4FWaofWOGwpmwYwuHduzYMS1YsEADBw5kINspd3d3DRw4UAsWLNDx48eNjgMATuP48ePMUDvHDIUzYccWDq1Zs2b6+eefdfLkSYayHbt//77effddVahQQfPmzTM6DgA4hebNm2vHjh3MUDvHDIWzYMcWDuvo0aP66aefNGjQIAaynXNzc7OuOB87dszoOADg8I4ePaqFCxcyQx0AMxTOgh1bOKymTZtq165dOnnypFKlSmV0HLyi6OhoeXp6qnz58lqwYIHRcQDAoTFDHQszFM6AHVs4pCNHjmjRokUaNGgQA9lBpEqVSoMGDdJPP/2kI0eOGB0HABwWM9TxMEPhDNixhUNq3Lix9uzZoxMnTjCUHUh0dLTy5cun0qVL66effjI6DgA4JGaoY2KGwtGxYwuHc/jwYS1evFiDBw9mIDuYuBXnxYsX6/fffzc6DgA4nN9//50Z6qCYoXB07NjC4TRs2FAHDhxQeHi4UqZMaXQcJLKYmBjly5dPJUuW1KJFi4yOAwAOpVGjRtq/fz8z1EExQ+HI2LGFQzl06JCWLl2qwYMHM5AdVMqUKTV48GAtXrxYhw8fNjoOADiMQ4cOacmSJcxQB8YMhSNjxxYOpUGDBjp48KCOHz/OUHZgMTExyp8/v7y8vLRkyRKj4wCAQ2CGOgdmKBwVO7ZwGAcPHtSyZctYaXYCcSvOS5cu1W+//WZ0HACwe8xQ58EMhaNixxYOo169ejp8+LCOHz+uFClSGB0HSSwmJkYFChTQBx98oGXLlhkdBwDsGjPUuTBD4YjYsYVD+PXXX7VixQoNGTKEgewkUqZMqSFDhmj58uU6ePCg0XEAwG4xQ50PMxSOiB1bOIS6devq6NGjOnr0KEPZiTx48EAFCxZUkSJFtHz5cqPjAIBdYoY6J2YoHA07trB7Bw4c0MqVK1lpdkIpUqTQkCFDtGLFCv36669GxwEAu8MMdV7MUDgadmxh92rXrq3jx4+z0uykHjx4oEKFCqlgwYJauXKl0XEAwK7Url1b4eHhOnLkCDPUCTFD4UjYsYVd27dvn1avXq2hQ4cykJ1U3IrzqlWrtH//fqPjAIDdiJuh7NY6L2YoHAk7trBrNWvW1KlTp3TkyBG5uroaHQcGefDggQoXLqx8+fJp9erVRscBALvADIXEDIXjYMcWdmvPnj1au3athg4dykB2cilSpNDQoUO1Zs0a7du3z+g4AGDz9u7dywyFJGYoHAc7trBbNWrU0NmzZ3X48GGGMvTw4UMVKVJEefPm1Zo1a4yOAwA2jRmKxzFD4QjYsYVd+uWXX7Ru3TpWmmHl6uqqoUOHau3atdqzZ4/RcQDAZjFD8W/MUDgCdmxhl6pVq6Y//viDlWbE8/DhQ7333nvKlSuX1q1bZ3QcALBJ1apV07lz53To0CFmKKyYobB37NjC7uzatUsbNmzQsGHDGMiIJ27Fef369dq9e7fRcQDA5sTNUHZr8W/MUNg7dmxhdwICAnThwgUdOnRILi6szSC+hw8f6v3331eOHDm0YcMGo+MAgE1hhuJ5mKGwZ/yLBruyc+dObdy4UcOGDWMg46lcXV01bNgwBQcHa9euXUbHAQCbwQzFf2GGwp6xYwu7UqVKFV26dEkHDx5kKOOZYmNj9cEHH+itt95ScHCw0XEAwCYwQ5EQzFDYK/5Vg934+eeftWnTJlaa8Z9cXFw0bNgwbdy4UTt37jQ6DgAYjhmKhGKGwl6xYwu74e/vrytXrujXX39lKOM/xcbGqmjRonr99de1adMmo+MAgKGYoXgRzFDYI/5lg13Yvn27tmzZwkozEixuxXnz5s3asWOH0XEAwDDMULwoZijsETu2sAu+vr66fv26Dhw4wFBGgsXGxqpYsWLKmjWrtmzZYnQcADAEMxQvgxkKe8O/brB5W7duVWhoqAIDAxnIeCEuLi4KDAxUSEiItm3bZnQcAEh2zFC8LGYo7A07trB5Pj4+unnzpvbv3y+TyWR0HNgZi8UiLy8vZcqUSSEhIUbHAYBkxQzFq2CGwp6wdAebFhYWprCwMAUGBjKQ8VJMJpMCAwMVGhqqrVu3Gh0HAJINMxSvihkKe8KOLWyWxWKR2WzWnTt3tG/fPoYyXprFYlHx4sWVPn16hYWFGR0HAJIcMxSJhRkKe8GOLWxWaGiotm3bxkozXlncinPc75oBgKNjhiKxMENhL9ixhU2yWCyqWLGioqKitGfPHoYyXpnFYlHJkiXl4eGhrVu38ncKgMNihiKxMUNhD9ixhU3asmWLduzYwUozEk3civP27dtZcQbg0EJCQpihSFTMUNgDdmxhcywWiypUqKCYmBjt3r2boYxEY7FYVLp0abm5uWnbtm383QLgcJihSCrMUNg6dmxhczZv3qyff/6ZlWYkurgV5x07dvBm8wAcEjMUSYUZClvHji1sisViUfny5RUbG6tdu3YxlJHoLBaLypQpoxQpUmjHjh38HQPgMJihSGrMUNgydmxhUzZu3Khdu3ax0owkE7fivHPnTm3atMnoOACQaJihSGrMUNgydmxhMywWi8qWLSuTyaSdO3cylJFk+LsGwNHw7xqSC3/XYKvYsYXN2LBhg3755Rd9/vnn/COJJGUymfT5559r9+7d2rhxo9FxAOCVBQcHM0ORLJihsFXs2MIm8DsbSG78LhoAR8EMRXJjhsIWsWMLm7B+/Xrt2bOHlWYkm7gV519++UUbNmwwOg4AvDRmKJIbMxS2iB1bGM5isahUqVJyc3PT9u3bGcpINnHv9xgdHa1ffvmFv3sA7E7cDHV3d+e9RZGsmKGwNezYwnBr167Vvn37WGlGsot7dce9e/dq3bp1RscBgBcWN0N5JWQkN2YobA07tjCUxWJRyZIl5eHhoa1btzKUkewsFosqVqyoqKgo7dmzh7+DAOwGMxRGY4bClrBjC0OtWbNG+/fvZ7cWhon7PaF9+/Zp7dq1RscBgARjhsJozFDYEnZsYRiLxaISJUooXbp0CgsLMzoOnJjFYpHZbNY///yjvXv38gMiAJvHDIWtYIbCVrBjC8OsWrVKBw4cUGBgoNFR4OTiVpz379+v1atXGx0HAP5T3Az9/PPPjY4CJ8cMha1gxxaGsFgs8vLyUsaMGRUaGmp0HECS5OPjo5s3b2r//v2sOAOwWXEzNFOmTAoJCTE6DiCJGQrjsWMLQ6xYsUIHDx5kpRk2JTAwUL/++qtWrlxpdBQAeKa4GcoZT7AlzFAYjR1bJLvY2FgVK1ZMWbNm1ZYtW4yOA8Tj6+ur69ev68CBA3JxYe0PgG1hhsKWMUNhJP7GIdmtWLFChw4dYrcWNunzzz/Xb7/9xoozAJvEDIUtY4bCSOzYIlnFxsaqaNGiev3117Vp0yaj4wBP5e/vrytXrujXX39lxRmAzWCGwh4wQ2EU/rYhWS1btkyHDx/m94Jg0wIDA3Xo0CEtX77c6CgAYMUMhT1ghsIo7Ngi2cTGxuqDDz7Qm2++qY0bNxodB3iuypUr69KlS/rtt99YcQZgOGYo7AkzFEbgbxqSzZIlS/T777/ze0GwC59//rl+//13LV261OgoAMAMhV1hhsII7NgiWTx8+FDvv/++cuTIoQ0bNhgdB0iQgIAAXbhwQYcOHWLFGYBhmKGwR8xQJDf+liFZLFmyREePHmWlGXbl888/15EjR7RkyRKjowBwYsxQ2CNmKJIbO7ZIcg8fPtR7772nXLlyad26dUbHAV5ItWrVdO7cOR06dEiurq5GxwHgZJihsGfMUCQndmyR5BYtWqRjx47xKo6wS4GBgTp69KgWL15sdBQATogZCnvGDEVyYscWSerhw4cqUqSI8uTJo7Vr1xodB3gp1atX19mzZ/X777+z4gwg2TBD4QiYoUgu7NgiSS1cuFDHjx9npRl2LTAwUMePH9dPP/1kdBQAToQZCkfADEVyYccWSebBgwcqXLiw8uXLp9WrVxsdB3glNWvW1KlTp3TkyBFWnAEkuYcPH6pQoULMUDgEZiiSAzu2SDILFy7UiRMnWGmGQwgMDFR4eLgWLlxodBQATmDBggXMUDgMZiiSAzu2SBIPHjxQoUKFVLBgQa1cudLoOECiqF27tsLDw3XkyBGlSJHC6DgAHBQzFI6IGYqkxo4tksT8+fN18uRJVprhUAIDA3XixAktWLDA6CgAHBgzFI6IGYqkxo4tEt2DBw9UsGBBFS5cWCtWrDA6DpCo6tSpo6NHj+rYsWOsOANIdMxQODJmKJISO7ZIdEFBQTp16hQrzXBIgYGBOnXqlObNm2d0FAAOiBkKR8YMRVJixxaJKiYmRgUKFNAHH3ygZcuWGR0HSBL16tXT4cOHdfz4cVacASSaBw8eqECBAnr//feZoXBYzFAkFXZskaiCgoJ05swZVprh0AIDA3X69GkFBQUZHQWAA5k7d65Onz7NDIVDY4YiqbBji0QTExOj/Pnzy8vLS0uWLDE6DpCkGjRooIMHD+r48eNKmTKl0XEA2DlmKJwJMxRJgR1bJJo5c+bo7NmzGjZsmNFRgCQ3bNgwnTlzRnPnzjU6CgAHwAyFM2GGIimwY4tEER0drfz586tEiRJavHix0XGAZNGwYUMdOHBA4eHhrDgDeGnMUDgjZigSGzu2SBSzZ8/WH3/8wUoznMqwYcN09uxZzZ492+goAOwYMxTOiBmKxMaOLV5ZdHS03n33XZUpU0Y//fST0XGAZNW4cWPt3btX4eHhSpUqldFxANiZ6Oho5cuXT6VLl2aGwukwQ5GY2LHFK5s1a5b+/PNPVprhlIYNG6Y//viDFWcAL2XWrFk6d+4cMxROiRmKxMSOLV5J3G5tuXLltGDBAqPjAIZo2rSpdu3apZMnT7LiDCDBmKEAMxSJhx1bvJIZM2bozz//1NChQ42OAhhm6NCh+vPPPzVz5kyjowCwI8xQgBmKxMOOLV7a/fv35enpqQoVKmj+/PlGxwEM1axZM/388886efKk3NzcjI4DwMYxQ4H/wwxFYmDHFi9t+vTpunjxIivNgB6tOJ8/f14zZswwOgoAO8AMBf4PMxSJgR1bvJT79+8rb968MpvNCgoKMjoOYBNatGihbdu26dSpU6w4A3gmZijwJGYoXhU7tngp06ZN019//cVKM/CYoUOH6uLFi5o+fbrRUQDYMGYo8CRmKF4VO7ZIsIcPH2r79u0qU6aM8ubNKz8/P82ZM8foWIBNadWqlUJDQ3Xq1Cnt3r1bFSpUkKurq9GxABiMGQr8N2YoXgU7tkiwsLAw+fj4aOzYsbp06ZKGDBlidCTA5gwZMkR//fWXxo0bJx8fH4WFhRkdCYANYIYC/40ZildBsUWC3b9/X5L03XffqW7duho5cqTOnDljcCrAdpw+fVpffvml6tSpo++++07So/epBABmKPB8zFC8KootEizurPUrV65o8+bNCg0Nlbu7u8GpANuROnVqhYaGasuWLbp8+bKk/3veAHBuzFDg+ZiheFUUWyRY3GqzxWKRr6+vfv31V7311lsGpwJsx1tvvaVff/1Vvr6+1mEc97wB4NyYocDzMUPxqii2eGFDhgzRsmXLlClTJqOjADYnU6ZMWrZsmfX351htBvA4ZijwbMxQvApeFRkv5MGDB0qRIoXRMQC7wPMFwOP4NwFIOJ4veFEUWwAAAACAXeNUZAAAAACAXWN//1/OnTunq1evGh0DiShr1qx65513jI6BRMDz0/Hw/HQsPEcdD89Rx8Hz0/Hw/IyPYvuYc+fOqWDBgrp7967RUZCIPDw8dOzYMZ74do7np2Pi+ek4eI46Jp6jjoHnp2Pi+RkfxfYxV69e1d27dxUUFKSCBQsaHQeJ4NixY2rZsqWuXr3Kk97O8fx0PDw/HQvPUcfDc9Rx8Px0PDw/n0SxfYqCBQvKy8vL6BgAnoLnJ2DbeI4CtovnJxwZLx5lYzw9PSVJBw8e1FdffZWsjz1w4EDlzJlT/v7+z71deHi4UqZMqR07dkiSpk6dKrPZLLPZrAIFCqhBgwbxbj906FDr5wU4AiOen/8WGBioggULWp970dHR8a4/d+6c9Tqz2ayUKVMqMjJS0dHRaty4sSpUqKBSpUpp06ZNkqQpU6aoVKlSqlixopo1a6b79+8b8WkBicIWnqOdO3e2Pv/eeOMNjR8//onblCtXTpUqVVLJkiW1YMECSY/et7NDhw6qWLGiqlatqgsXLkiSZsyYIW9vb1WsWFG1atXSrVu3kvXzAV5GRESEVq1a9ULH3LhxQ3PmzLH+edasWdZZlVxu3rypMmXKKGPGjAoKCrJefubMGVWsWNH63P7jjz+eOLZBgwaqVKmSSpQooXHjxiVnbFhgtX//fosky/79+w3LkDdv3kS5n9u3b7/wMRcuXLCcPn3a4ufn99zbNW3a1OLv72/Zvn37E9d16NDB8tNPP1n/fP78eUvTpk0T7fN6UbbwPUXisJXv5YMHDxL9Pu/evWt5+PDhCx0zbNgwy9y5cxN0259//tlSrVo1i8VisaxevdrStm1bi8VisZw7d87i5eVlsVgslpMnT1oz9OnTxzJt2rQXyvMybOV7isRhK99PW3mOPq5gwYKWixcvPnH5/fv3LRaLxXLz5k1Lrly5LBaLxbJ8+XJLx44dLRaLxbJ9+3ZL69at493WYrFYhgwZYpkwYcJL50koW/me4tUZ9b0MDQ21fPTRR0+97lnP1bNnz/7nz6Iv6kV/Lo6OjrZcunTpiVn72WefWWbNmmWxWCyWuXPnWnr16vXEsXHP1ZiYGIunp6fl1q1br5D82Xh+PokdW4PFxsbqww8/VIUKFdS+fXvFxMRIksLCwtS+fXtJ0ogRI1SqVCn5+vpq7Nixz7yvO3fuKCgoSLVq1VLv3r1fOMtbb70lF5fn/5XYtm2bcuXKpbfffvuJ66KiorRx40bVrl3bellgYKAGDx78wlkAIw0aNEhly5aVj4+Pli5dqoiICJUsWVKtW7dWy5Yt4z0/27Ztq48//lg1a9ZUiRIltGzZMlWrVk2FCxfWzp07n/kYDx8+1KZNm9S2bVtVrVr1pXZIx4wZI29v7/9cEZ4zZ45atWolScqbN6/u378vi8WiyMhIvfbaa5IenS0S9/x3c3OTq6vrC+cBkou9PEclaffu3cqRI4fefPPNJ65LlSqVJOn27dsqXLiwJOnEiRMqUaKEJKlEiRIKDQ2Nd1vp0byPuz1gCyIiIlS8eHE1adJEpUuX1tChQyVJY8eO1dq1a2U2m7Vjxw61bdtWnTp1Uq1atRQcHKyBAwfK19dXXl5emjx5svWY/fv3y2w2a8mSJQoMDLTums6cOVOlS5dWmTJlNGrUqOdmOnXqlIYPHy5vb2+FhYW90OeTMmVKvf76609cXrhwYd24cUOSdP36desMfVzcc/Xu3bt655135OHh8UKPjZfH79gabNWqVUqRIoW2b9+ukydPau7cuU/cZt68edq3b5/SpEmj2NjYJ67fuHGj5syZo6tXr6p27dqaMWOGsmXLJknas2eP+vbt+8QxXbt2VcOGDV8oq8Vi0ciRI7Vw4UL16NHjietXr14tf39/ubu7S5IOHTokSQxf2JX169fr9OnT2rlzp0wmkx4+fKg///xTERER2rx5szJkyPDEgCxUqJCmTp2qESNGaO7cuVq/fr127Nih77//XuXKlYt326NHj2ratGk6cOCAzGazBgwYoPz580uSoqOjVaVKlScyeXt7a8SIEfEu69atm4YNG6b79++rTp06Klq0qHx8fJ44Njo6WuvXr7eW3zx58uju3bsqUKCAbty4oZUrV8a7/bFjx7R+/Xpt3br1hb92QHKwl+donLlz51oXlv7t3r17CggI0JEjR6w/pBcpUkQzZ87URx99pA0bNuj69evW20+ePFkTJ06Uh4eHBgwYkOCvGZAcIiIitGXLFqVPn15+fn76/fff1atXLwUFBWnatGmSpGnTpil79uzWElupUiWlSZNG9+/f13vvvaf27durV69eOnr0qDZv3ixJ+v333yVJV65c0bhx47R3716lSJFCvr6+qlWrVryfM+/cuaNZs2Zp9erVeuONN9S0aVMNHDhQKVI8qjzDhw9XSEjIE9nXrFmjtGnT/ufn6Ofnp4CAAE2fPl1RUVHas2fPU29Xp04d7dq1S506dWKhOBlRbA0WHh6uMmXKSJLeffddayF93IQJE9SlSxc9ePBAn3zyiby9veNdP2/ePJ0+fVrdunVT7dq14z0xS5Uq9cKrVM+ycOFC+fv7K2PGjE+9fs6cOfF2iocOHaoJEyYkymMDyeXw4cPy8/OTyWSSJOtAKlKkiDJkyPDUY4oXLy5Jyp49uywWi/Xja9euPXHbkJAQrVu3Th07dlSTJk301ltvWa9LlSpVgp+vWbJkkSS5u7urQYMG2rdv31OL7erVq+Xr66vUqVNLevS7StmzZ9eKFSsUERGhevXq6ddff5X06IeSNm3aaNGiRUqTJk2CcgDJzV6eo9KjIrx27VqNGTPmqdenTp1a27Zt09WrV1WyZEk1btxY1atX186dO2U2m1WyZEkVKFDAevtOnTqpU6dOGjVqlL766qtn3i9ghIIFC1p/RixdurTCw8Ots+pxj/8cO3nyZK1YsUKurq66fPmyLl++/Mz7P3PmjN5//325ublJksqUKaPw8PB4xfbixYuaOnWqSpcurXbt2qls2bLx7mPo0KHW3eSX0a9fP/3vf/9Tw4YNtXDhQg0YMMBa0h+3cuVK/fPPP6pYsaKaNGmiQoUKvfRjIuEotgbLly+f1q9fr48++kinT5/WlStXnrhN2bJl5efnp3PnzqlevXrav39/vOtnz56ta9eu6aefflK9evWUNWtWtW3bVgEBAYm6Y3vw4EHt27dPmzdv1uHDh3X8+HHNnz9fefLk0ZUrV3Ts2DFVrFjRevvTp09bTwX766+/1KVLF02cOPGFHhNIbkWKFFFQUJDat29v3Q2S9NwV17gfsP/9cdwP0I/r2rWrOnTooDVr1qh79+76559/VL9+fbVr104PHz5M8G7QjRs3lDFjRlksFoWGhqpZs2ZPzTZ37lx179493mVxC2iZMmXSnTt3JEmXLl1So0aNNH36dOXJk+eZnytgNHt5jkrSunXrVLFixacuFEVHRytFihRycXFRmjRp5O7ubj3jKe6+goODrac1RkVFWa/PlCmToqKinvn5AkY4duyYbt68qfTp02vPnj1q0aKFbt++rQcPHsS7XdxzNTIyUjNnztShQ4f+X3t3Hh/T2fYB/JcEiVgTuyIhEbtaguw5kwQtStVSS6kqquj66kpLParebs/TvVWeKlrdUUqpOicEsSQlQVBLqvYtQUT2+/0j75xmZBJZZuacM/l9P59+yqz35HLnmuu+7nMOcnNz0a5dOwghUKNGjWLPAQp3HO3fvx/Z2dmoXr064uPjMX78eIvHBAQEICkpCfv27cOKFSvw3HPPITw8HFOnToWPj0+lO7bAPzm0UaNG6rZks4KCAuTn56N69eqoWbOm+h85BgtbjQ0ZMgSrV69GeHg4OnfubPUYnKFDhyIrKwtZWVmYPn261ddp0KABpk2bhmnTpuHkyZPqGYvL07F977338P333yMlJQUxMTH4+OOPERAQgLFjx+Krr77C//7v/6qPnTBhAiZNmqR+Af7mm28wcuRIiy8MycnJ6p/9/f1Z1JIhDBgwALGxsQgODoanpyemT5+udntsxd3dHcOGDcOwYcOQnp6O77//Hjk5OahZs2aZ5+vTTz+NI0eOQAiB8PBw9dj2p59+GrNmzUKjRo1w5coVJCUlQZIk9XkPPfQQRo8ejcjISGRmZuL1118HALz44ou4cOGCWgSPGTMGU6ZMsennJrIFo8xRoHBh6bHHHrO4zTxHr127hokTJ8LNzQ3Z2dmYM2cO3N3dkZaWhqFDh8LNzQ0+Pj7qzqd58+apxwR7e3vjv//9r80+L5EttGnTBo8//jiOHz+OmJgYdOnSBTdu3MDx48cxfPhwvPDCCxaPr1+/Pjp27IiwsDB07NhR7e42bdoUNWvWxLBhwzBx4kT18Y0aNcJTTz2F8PBwuLi4YPDgwSUe7tatWzd069YNBQUFkGUZZ8+ehY+PT7k6tgMGDMDhw4fh6emJ2NhYfP7555g9ezYee+wxVKtWDbm5ufjss88AAAsXLsTAgQPh6+uL++67DwCQnZ2NUaNGoXXr1uX+WVLFuAhry5VVVGJiInr27ImEhARe48tJMKbOg7F0Poypc2E8nQ9j6jzsHcvU1FRMmjRJPS6W7I/zszieFZmIiIiIiIgMjYUtERERERFVmK+vL7u1pDkWtkRERERERGRoLGwNoujFqfVuzZo16NOnDyIjIzFw4ED1cgrZ2dkYP348wsPDcf/99+PatWvFnvvAAw9AkiRIkoT69etj7dq1OHXqlHqbJEmoXr060tLSABReliEmJgYmk4nX9CNNGWmOHj16FD169EDt2rXVE80BQH5+Pl566SXExMRAkiSr17JNSkpCWFgYQkJC8M4771jcl5OTA39/f4uzw86ZMwchISGQJEm9FiGRoxlpfppt2bIFLi4uOH36tMXtS5YsQfXq1a0+54MPPkBAQAD8/f2L3Xf7/Ny9e7eaV4OCgqxeloXIUZxhjn722Wfo3bs3IiIiMHr0aGRnZxd7zt9//42YmBiEh4dbXCJz2LBhiIyMRGBgoHrdeQCIiYlBo0aNSrxONlliYasx82UKHPk+Qgirlzgo7TnlcffddyMuLg6xsbEYOHAg/vOf/wAovCyRr68vtm3bhgEDBuDdd98t9tyffvoJiqLg119/Rf369dGvXz+0atUKiqJAURQsWLAAffv2hZeXF65cuYJ33nkHv/zyC2RZxhtvvFGh8RKVxhnnaIsWLfD7778Xu+TX4sWL0bJlS2zevBmKoiAyMrLYc6dPn44vvvgCcXFxWLNmDY4fP67e99FHH1lcq2/fvn3YvXs3duzYgaVLl+Kpp56q0HiJSuKM8xMovGTI22+/jcDAQIvbMzMz8dNPP6FVq1ZWnzdy5EgcPHjQ6n23z0/zVRMURcGTTz6JBx98sMLjJSpJVZqj0dHRiI+Px9atW9GyZUurhfoLL7yAl19+Gdu2bcPZs2chyzIAYOXKlYiNjUV8fDw+/vhj3LhxA0DhteffeuutCo+zqmFhW4rU1FSEhobCZDJBkiSkpaXhwoULiI6ORr9+/TBt2jTExMQAKLz8jbnzoSiKev3W9957D1FRUQgMDMTs2bPV1/bx8cH06dPRr18/XLt2DSNHjkRUVBRMJhOOHj0KAFi1ahW6deuGwYMHF7t2bVG5ubmYPHkyoqKiEBYWhp07d6pjevzxx3Hfffdh48aN8Pf3x6xZsxAdHY3Tp09j1KhRiIyMRGhoqPr6tz+nInx9fdXVZHd3d/V6ZbIsY+jQoQCA+++/X53M1qxevRr9+/dXL8JttmzZMowbNw4A8Msvv6BRo0a4//77ERMTg/j4+AqNl4yLc7Ric9TT0xNeXl7Fbv/2229x7tw5REVFYeLEiWpiNcvOzsb169fRtm1buLq6YtCgQWpXNz09HZs3b8YDDzygPv7o0aPqZVh8fX2RkpJi9dqE5Jw4Pys2PwFgxYoVGDx4cLHr37799tt44oknLC6tV1STJk2sdnOtzc+iiuZWqjo4R207R/39/eHqWlhaFf3+W1RiYiKioqIAWH4XNl+vOjMzE61atYKnpyeAwoVoKjsWtqXYsmUL+vbtC1mWoSgK6tevjzfeeAMTJ07Epk2biq2kWjNp0iRs2bIFe/bswe+//46//voLAHD27Fk899xz+P333/HGG2/ggQcewJYtW/Dhhx/i+eefR0FBAV588UXExsZi9erVxb5gFrVkyRL4+flhy5YtWLVqFZ599ln1vhYtWmDt2rUYMGAA8vLyMGDAAGzZsgU//fQT2rdvj9jYWPz3v/+1uD5u0ecUNW3aNIstwZIkYdCgQSWO6/z58/jwww/Va2FeuXJF/TJt7riWZPny5cWSbE5ODjZs2ID7778fAHDmzBmkpKRg1apVWLJkicW1zqhq4Byt3By93ZkzZ+Dt7Y0tW7agY8eOxVaJi85hwHIev/7668WuUdipUyfIsoycnBwkJibi/PnzxS5mT86L87Ni8/PWrVtYtmyZWjiYnT9/HgkJCbjnnnvu+HO7nbX5WfR1U1NTERwcXO7XJWPjHLXtHDVLSUnBhg0bMGLEiGL3FRQUqH++/bvwkCFD4O/vj7CwMKtFMd1ZNa0HoGcjR47EggULMHbsWPj4+GDu3Lk4cuQInnjiCQBASEgIvv76awCwWD0tuv3hxx9/xOLFi+Hi4oKTJ0/i9OnT8PHxQYsWLeDr6wsASE5ORmxsLD799FP1tS5duoTGjRujXr16AICgoKASx5mcnIwdO3bg119/BQCLL45hYWHqn93c3BASEgIAOHz4MIYNGwYAaNeuncXEKvqcoj7++ONSflqW0tLSMGzYMCxatAjNmzcHADRo0ABpaWnw9fVFenp6icfzXLhwAX/++SdCQ0Mtbl+7di2ioqJQs2ZNAIUXqDeZTPDw8ICPjw/q1q2Lq1evwtvbu8zjJGPjHLVUnjlqjbe3N+69914AwKBBg/D8888Xu7/o2M3zODU1FSdPnkRERAROnDih3t+pUyeMHj0aMTExCAgIQJcuXXgcXxXC+WmprPPzP//5D6ZOnYpq1Sy/os2dO9eiI1ZWJc1Ps6+++gqjR48u9+uS8XGOWqrsHAUK59vDDz+M7777rtiOCwBqR9f8OYrmxDVr1uDmzZuIiIjAgw8+aHHoAJUNC9tSuLq6YsGCBQCAiRMnYuPGjQgICMCuXbvg5+dnsfXV29sbp06dAgDs2bNHvf2VV17B4cOH4e7ujtDQUPWXQdGVmE6dOiE4OFjdppuTkwM3NzdcuHAB169fR506dbB792507tzZ6jg7deoEf39/PPPMM+rzzYq+j4uLi/qLqX379ti2bRtiYmJw5MgRi4lV0irRtGnTcOjQIYvbateujXXr1lnclpGRgSFDhuC1115Dr1691NslScLPP/+M7t274+eff4YkSVbfZ+XKlRg1alSxrVbLly/Hk08+qf7dZDLhxx9/hBAC165dQ1pamtXtleS8OEctlXWOliQ6Ohp79+5F+/btsWvXLgQEBFjc7+HhgTp16uDEiRNo3bo11q9fj88//xyJiYk4e/Ys7rnnHpw5cwbZ2dno3Lkz7r//fsyYMQMzZszAgQMH8NZbb5W4hZKcD+enpbLOz4MHDyI2NhaLFy9GUlISxo0bh3Xr1uHYsWN45ZVXAADnzp3D8OHD8cMPP1h9r6JKm59A4ZbKsrwOOR/OUUuVnaM3btzAiBEjsGTJErRp08bqe3Tv3h2xsbGIjIzEzz//jEceeQQFBQXIz89H9erVUbNmTfU/Kj8WtqVYt24dPvjgA7i5ucHd3R1hYWEIDAzEmDFj8MUXX1hMwEmTJmHMmDH4+uuv0bp1a/X2Bx54AKGhoWjfvj1q165t9X1mzZqFqVOn4oMPPgAA9O/fHy+88ALeeOMNREREoGXLlqV2ISdPnowZM2bAZDIBKJw01k7MdPtzJkyYgIiICOTl5anvXZqyrmS9/fbbOHz4MObPn4/58+cjKioKr776KiZMmIBJkyYhIiICXl5e+PLLLwEUHhh/1113oW/fvgAKC9iVK1davOaVK1eQlJRkUQwHBATg3nvvRWRkJLKzs/Huu+/yS3MVwzlqqaxzNC0tDSNGjMChQ4dw8OBB9O/fH/Pnz8fMmTPx6KOPYsmSJfDw8MCyZcsAAAsXLsTAgQPRpUsXvP/++xg/fjwKCgowdOhQ+Pv7w9/fXz12b+nSpTh9+rT6pblfv37Iy8tDw4YN8dFHH5VpfOQcOD8tlXV+Fj3hjCRJWL58OWrVqmVxjVB/f3+1GC2aQ7/55hssXrwYZ8+eRUxMDObMmYMHHnigxPmZnJwMT09P+Pn5lWls5Fw4Ry1Vdo5Onz4dFy5cUJswY8aMwZQpUyzm6MKFCzFx4kTk5OQgMDAQJpMJGRkZuO+++wAUnsti1KhR6s944sSJ2LVrF7Kzs7Fr1y6sXbu2TGOsqlxEWU4bVkUkJiaiZ8+eSEhIQI8ePe74+NTUVEyaNIkXpNax8saU9KsiseQc1TfOT+fCHOp8OEedB3Oo8+H8LI4dW4MZP368uhUEKNwa8tNPP2k4IiIqinOUSL84P4n0jXOUKoOFbSX4+vo6fBXLvDWQiO6Mc5RIvzg/ifSNc5SMhpf7cVL+/v4OeZ+4uDh06dIFHh4eOH36tHr7rVu3MHXqVMTExECSJBw5cgQ5OTkWp1D38PBAcnKyQ8ZJpCdaz88PPvgAAQEBFuM4deqUxfysXr060tLSHDJOIj1z1Hw1W7JkicW1aItegqRp06ZlOlaQqKpw1Pz87LPP0Lt3b0RERGD06NHIzs4GUHgprYiICISGhmL8+PHIzc11yHjIOha2VCldunTBzp07i52mfd68eRg8eDA2b94MRVHQrl071KhRA4qiQFEUrFixAv7+/ujSpYtGIydyfiXNz5EjR+LgwYMWt7Vq1UqdnwsWLEDfvn15lnEiB8vMzMRPP/2EVq1aqbd9/PHH6tz09vbG8OHDNRwhUdUUHR2N+Ph4bN26FS1btlRPIPXcc89h69at2L59OwBg06ZNWg6zyuNWZAdLTU3F2LFjUaNGDQghsGrVKiQnJ2POnDnIy8uDl5cXvv32W9SsWROSJKF79+44dOgQsrOzMWXKFHz55Ze4cOECvvvuOwQEBECSJHTq1AlHjx5FQUEBVq5cicaNG6vvl5ubi2nTpuH48ePIycnBW2+9heDgYMyfPx8///wzateujUGDBllc7Lo8zNcfu92mTZuQl5eHN998Ez169MCbb75pcb2vFStWYOzYsRV6TyJ7qSrzs0mTJqU+b9myZRg3blyF3pPIUZxtvgKFVxV44oknMGPGjGL3xcfHo2XLlmjWrFmFX5/IUZxtfhbtDLu7u6uXDKpRowYAoKCgAHl5eQ7f4UGW2LF1sC1btqBv376QZRmKoqB+/fro2bMnZFnGtm3b0KFDB3z33Xfq4yMjI7Fx40b4+/tjz5492LhxI2bOnIn//ve/6mP69OmD3377DWPHjsVbb71l8X5LliyBn58ftmzZglWrVqkT+quvvoIsy9iyZQuefvrpYuN84IEHLLYlSpKESZMmlflzHjhwACEhIVAUBbm5ucWOmfjqq69Y2JLuVJX5WZqcnBxs2LBBvRwIkV4523w9f/48EhIScM8991j9vMuXL+eCExmGs81Ps5SUFGzYsAEjRoxQb5szZw4CAgKQnp6Oli1bVvRHRjbAjq2DjRw5EgsWLMDYsWPh4+ODuXPn4uDBg5g9ezays7Nx4cIF1K1bV318z549AQAtWrRQrzPXokULxMbGqo8JCQlR/79q1SqL90tOTsaOHTvw66+/AgDS09MBAB9++CGmT5+OvLw8TJ06FWFhYRbPq+wZ6Ly9vXHvvfcCAAYOHIj169er9yUkJKBhw4YWW62I9KCqzM/SrF27FlFRUbw4POmes83XuXPnYvbs2Vbvy8nJwS+//II333yzTK9FpDVnm59AYRf64YcfxnfffYdatWqpt7/22muYO3cupk+fjqVLl2LatGllfk2yLRa2Dubq6ooFCxYAKLzo8saNG7F48WK89tprCA4OxvPPP4+ilxZ2cXGx+ueij4mPj4e/vz/i4+PRrl07i/fr1KkT/P398cwzzwAoTI4AEBwcjOjoaJw6dQpDhw5FQkKCxfMeeOABXL161eI2f39/LF68uEyfMzo6Gnv37kVYWBh27dqFgIAA9T6uOpNeVZX5WZrly5erF5cn0jNnm6/Hjh3DK6+8AgA4d+4chg8fjh9++AEAsH79ekRERFh8mSbSM2ebn+fPn8eIESOwZMkStGnTRr09KysLHh4ecHFxQb169eDp6Vn2HxLZHAtbB1u3bh0++OADuLm5wd3dHWFhYcjIyMCjjz6K9u3bo27duhYrWGWRmJiIL7/8Evn5+Vi5cqXFfZMnT8aMGTNgMpkAAN27d8e7776LoUOHIisrC1lZWZg+fXqx1yzrClZKSgqeeOIJ7N+/H6NHj8aDDz6IGTNmYOHChZg0aRKysrLQuHFjdStyXl4eVq9ejXnz5pXrMxI5QlWZn9988w0WL16Ms2fPIiYmBnPmzEF4eDiuXLmCpKQkSJJUrs9IpAVnm69FL6vi7++vFrVA4YLTY489Vq7PQqQlZ5ufL774Ii5cuKAu/I4ZMwZTpkzBtGnTcOLECeTn5yMgIIDfbzXmIoouhVRxiYmJ6NmzJxISEtCjRw+th1MmkiRhxYoVaNGihdZD0SUjxpSsM2IsOT9LZ8SYUsmMHk/O1+KMHlP6h9FjyflZnNFjag88eRQREREREREZGrciG5yiKFoPgYhKwPlJZBycr0T6xflJZcGOLRERERERERkaC1udSk1NRUxMjMPeb+7cuejTpw9CQ0Px5JNPqmehmzJlCoKCghAUFISFCxdafe5nn32GPn36IDw8XF1Ry8nJwciRIxEeHo7evXvjt99+AwDExcWhS5cu8PDwwOnTpx3y2YjswdFz9IcffkCHDh3g4eFh9f6IiAir194TQmDGjBkIDg5Gr169sGLFCvX2Z599FuHh4YiJiVHn44kTJxAREaFez++vv/6y34cishNHz8/XX38dERERCA0Nxfjx45GbmwsASEpKQlhYGEJCQvDOO+9Yfe6cOXMQEhICSZJw4MABAMCaNWvQp08fREZGYuDAgbhy5QoA5lByDo6en2avvvoq/P39i90+fvx4q+M5deqUxfVtq1evjrS0NADAW2+9hdDQUISHhyM5ORkAkJmZieHDh0OSJAwbNgzXrl2z7weiYljYEoDCs7vt2rUL27dvx/nz57FlyxYAwMyZMxEfH48dO3ZgzZo1OH78uMXzLl68iM8++wxxcXFYt24dZs6ciYKCAmzatAm1atXCtm3b8OOPP+LFF18EAHTp0gU7d+5EUFCQwz8jkZFJkoQ//vjD6okzVq1ahfr161t93sGDB3Hw4EHs3LkTiqLg1VdfBQD89ttvuHz5MrZt24bnn38es2bNAgB8/PHHePTRR6EoCiZNmoT333/fbp+JyFk899xz2Lp1K7Zv3w4A2LRpEwBg+vTp+OKLLxAXF2c1h+7btw+7d+/Gjh07sHTpUjz11FMAgLvvvhtxcXGIjY3FwIED8Z///AcAcyhRRZ05cwZ//vlnsdsTExNx/fp1q89p1aoVFEWBoihYsGAB+vbtCy8vLxw9ehTr169HXFwcPv/8c/VMyYsWLUJgYCAURcHQoUPx7rvv2vUzUXEsbB1o5syZ+PHHHwEUXvama9euyM3Nxcsvv4yoqCj06NEDn3zySbHnTZgwAXFxcQCgftkEgAMHDiAmJgZRUVEYMWIEMjMzKzy2oteZdXd3h5ubm8Xtrq6uqFatmnq7WWpqKjp27Ijq1aujXr16qFatGlJTU+Hn54fs7GwIIZCWlobGjRsDAOrVq4fatWtXeJxE9qTnOdqwYUOr3dq8vDx88sknVi9jAADNmzdHjRo1kJubixs3bsDb2xsAIMsyhg4dCgDo27cvdu/eDaDwWoDmC9tfvXpVnbtEWtPz/KxRowYAoKCgAHl5efD390d2djauX7+Otm3bwtXVFYMGDUJsbKzF844ePYqePXsCAHx9fZGSkoK8vDz4+vqievXqACxzMnMo6ZWe5ydQuDNx9uzZxW6fN2+eurBbmmXLlmHcuHEACvPn4MGD4eLigvbt2+PSpUvIy8vD0aNHERgYCAAIDAyELMuVGjOVH08e5UATJkzAyy+/jGHDhmHjxo2IiopC9erVMWvWLNSqVQvZ2dno0qWL1e2E1kybNg0rVqxAq1at8NFHH2HRokV4+umn1ftzcnLQr1+/Ys8LCwvD/Pnzrb6moig4ffo0IiIiLG5fsWIFWrZsCV9fX4vb/fz88Mcff+D69eu4fv06Dh48iKtXr6JLly7IzMxE+/btkZ6ejjVr1pTpMxFpyQhz9HaffvopHnroIbi7u1u938vLC35+fggICMDNmzfx2WefAQCuXLkCLy8vAICLiwvy8/MBANHR0ejfvz+WLFmCrKwsteAl0pre5+ecOXPw1VdfISAgAC1btrSYY0DhXDRvKTbr1KkT3nvvPeTk5ODAgQM4f/480tPT0bBhQwDA+fPn8eGHH+KXX34p02ci0oqe52dSUhKAwvlW1Lp169CjRw80atSo1LHk5ORgw4YN+Pe//w2gMH82b95cvb9evXpIS0tD586d8euvvyImJga//vorrl69WqbPSrbDwtaBOnfujEuXLuHixYv48ssv8dJLLwEAPvnkE6xevRpubm64ePEiLl68aPE8FxcX9c9FLzt88OBBjB8/HgCQnZ0NSZIsnlejRo1ynUUuMTERL730EtatWwdX13+a+Rs2bMCyZcvw888/F3tOgwYNMHfuXAwaNAjNmjVDt27d0Lx5cyxduhQtWrTA6tWrkZqaiqFDh+KPP/4o81iItKD3OXq769evY/Xq1di0aRO2bt1q9TGbNm3CmTNncOzYMVy7dg1hYWEYMGAAGjRooB4rJIRAtWqF6eCFF17Av/71LwwfPhzffPMNXnrpJaur7ESOpvf5+dprr2Hu3LmYPn06li5diokTJ6q7HwAgPT0dDRo0sHhOp06dMHr0aMTExCAgIABdunRRH5OWloZhw4Zh0aJFFl+iifRIz/Pz1VdfxYcffmhxW35+Pt59912sXbsWly5dKvX5a9euRVRUFGrWrAkAFvkTKMzFXl5eePTRR/HMM8/AZDIhODiY81YDLGwdbOzYsfjoo4+QmpqK7t27Iy0tDV988QWSkpKQm5uLdu3aWUxsAPD29sapU6cAAHv27FFv79y5M1auXIlmzZoBKFxRKqo8q1kpKSmYMmUKVq9ebZF4t27divnz52P9+vUlnrRmxIgRGDFiBM6dO4dJkyapE9m8Aubl5YWMjIwy/XyItKbXOWpNSkoKrl+/jgEDBuDq1as4d+4cPvvsMzz22GPFxufm5oY6deogNzcX+fn5kCQJ33zzDYYOHYotW7ao26eAf+Zuo0aNLL6YE2lNr/MzKysLHh4ecHFxQb169eDp6QkPDw/UqVMHJ06cQOvWrbF+/Xp8/vnnxV5vxowZmDFjBg4cOIC33noLLi4uyMjIwJAhQ/Daa6+hV69eFfthETmYXufn8ePH1U7xuXPnMH36dLz88svq4tGtW7dw8OBBzJs3Tz0PRVHLly9Xj6MFCs95MX36dDz99NM4duwYGjRogGrVqqFatWr4+OOPARSeWPX2XY7kAIJUCQkJAoBISEiw23tcuXJFeHp6irffflsIIURBQYEYPny4CAoKEhMnThTdu3cXf//9tzh58qSIjo4WQghx6NAh0a1bNzFw4EAxY8YM8eijjwohhEhOThb9+vUTJpNJmEwmsWHDhgqPKzIyUrRt21ZERkaKyMhIsWbNGiGEED4+PqJLly7q7bt27RJCCPHUU0+JixcvCiGEGDdunJAkSQwcOFAcP35cCCFERkaGuO+++0RERIQIDAwU3377rfpZoqOjRf369UVYWJj44IMPKjzmsnBETMkxHBVLvc5RWZZFdHS0qFmzpoiOjhbfffddsfvN7yuEEGPGjBFCCJGXlycefvhhERISIgIDA8V7772nfq6nnnpKhIWFiejoaHHq1CkhhBAHDhwQoaGhIjIyUoSEhIjk5OQKj/lOOD+dS1XOoY888oiIjIwUYWFhYuLEiSInJ0cIIURiYqIIDQ0VwcHB4s0331Qfb56fQgjRt29fYTKZxIgRI9S8OmfOHNGoUSM197722mvqZ2EOpYqoyvOzKD8/v2K3FR2PEJbfcS9fvixat24t8vPzLZ6zcOFCERISIkJDQ8W+ffuEEEIcPHhQREZGiqioKPHss8+KvLw8m4y5JJyfxbkIcdvSSRWWmJiInj17IiEhAT169NB6OGQDjKnzYCydD2PqXBhP58OYOg/G0vkwpsXxrMhERERERERkaCxsiYiIiIiIyNBY2BIREREREZGh8azIVqSkpGg9BLIRxtL5MKbOg7F0Toyr82AsnQ9j6jwYy+JY2BbRsGFDeHp64qGHHtJ6KGRDnp6e6sXuybg4P50T56fz4Bx1TpyjzoHz0zlxflriWZFvc+rUKVy+fFnrYdjEo48+Ci8vL7z99ttlevyff/6JUaNG4dNPP3Wq6+Y1bNgQrVq10noYZAPOND93796Nxx9/HN9++y38/f3L9JyZM2ciPT0dixcvtvPoHIfz07k40xydNGkSvLy88NZbb5Xp8cyhpHfOND+ZQwtxflpiYeukMjMzUb9+fbz77ruYMWNGmZ5TUFCAxo0bY9q0aZg3b56dR0hUtb3yyiv49NNPceHCBbi6lu10Bx988IGamGvWrGnnERJVXcyhRPrGHErW8ORRTmrnzp3Izc2FJEllfo6rqysiIyOhKIrdxkVEhRRFQWRkZJkTMgBIkoScnBzs3LnTjiMjIuZQIn1jDiVrWNg6KVmW0bBhQ3Tq1KlczzOZTIiPj0dmZqadRkZEmZmZ2LVrF0wmU7me16lTJzRs2BCyLNtpZEQEMIcS6RlzKJWEha2TUhQFkiTBxcWlXM+TJAm5ublczSKyox07dpS7GwSwI0TkKMyhRPrFHEolYWHrhG7evIndu3eXeyUL+Gc1i5OeyH4URUGjRo3QsWPHcj/XZDJh165d7AgR2QlzKJG+MYdSSVjYOqGKrmQBgIuLCyRJ4jYNIjuSZblC3SDgn47Qjh077DAyImIOJdI35lAqCQtbJ6QoCho3bowOHTpU6PmSJGH37t24efOmjUdGROZuUEW+NANAx44d0ahRI3aEiOyEOZRIv5hDqTQsbJ1QZVaygMJtGlzNIrKP7du3Iy8vr0LbHAF2hIjsjTmUSL+YQ6k0LGydTEZGBvbs2VPhlSwA6NChAxo3bszVLCI7UBQFTZo0Qfv27Sv8GuwIEdkHcyiRvjGHUmlY2DqZyq5kAVzNIrKnynaDgMKOUF5eHrZv327DkRERcyiRvjGHUmlY2DoZ80pWu3btKvU6kiRhz549yMjIsNHIiMgW3SAAaN++PZo0acKOEJGNKYqCpk2bMocS6RBzKN0JC1snY4uVLOCf1SweI0RkO9u3b0d+fn6lukHAPx0hJmUi26ro9WtvxxxKZHvMoXQnLGydyI0bN7B3795KT3gAaNeuHZo0acKtVEQ2JMsymjZtioCAgEq/FjtCRLZ148YNm3SDAOZQIntgDqU7YWHrRMwrWbZIylzNIrI9W3WDgMKkzGOEiGyHOZRI35hD6U5Y2DoRWZbRrFkzm6xkAYVbqfbs2YMbN27Y5PWIqjJb7qgACjtCTZs2ZUeIyEaYQ4n0izmUyoKFrROx5UoWULialZ+fz9UsIhuIi4uzWTcIYEeIyNaYQ4n0izmUyoKFrZO4fv06EhISbLaSBQABAQFo1qwZJz2RDSiKgubNm6Nt27Y2e02TyYS9e/eyI0RUScyhRPrGHEplwcLWSdh6JQvgtfiIbMlWZywvytwRiouLs9lrElVFzKFE+sYcSmXBwtZJyLKM5s2bw9/f36avK0kSEhIScP36dZu+LlFVYu4G2fJLMwC0bduWHSEiGzB3g5hDifSHOZTKioWtk1AUBSaTyaYrWUDhNg2uZhFVzrZt21BQUGDTbY5AYUfIZDKxI0RUSbIsM4cS6RRzKJUVC1sncO3aNSQmJtp8JQsA/P390bx5c65mEVWCoii466674OfnZ/PXZkeIqHKYQ4n0jTmUyoqFrROw10oW8M9qFpMyUcXZa0cFUNgRKigoYEeIqILi4uKYQ4l0jDmUyoqFrRNQFAUtWrRAmzZt7PL65tWsa9eu2eX1iZyZPbtBAODn54e77rqLW6mIKkiWZeZQIp1iDqXyYGHrBOxxpriiuJpFVHH23FEBsCNEVFn27AYBzKFElcEcSuXBwtbg0tPT8ccff9htwgNAmzZt0KJFC65mEVWALMto2bIlWrdubbf3kCQJiYmJ7AgRlZM5h9qrGwQwhxJVBnMolQcLW4Pbtm0bhBB2Tcrma/FxNYuo/BRFseuOCqAwKRcUFGDbtm12ew8iZ2TuBjGHEukTcyiVBwtbg5NlGa1atbLrShZQuJXqjz/+QHp6ul3fh8iZpKWl2X1HBVDYEWrZsiU7QkTlxBxKpF/MoVReLGwNzhErWQBXs4gqwhE7KgB2hIgqijmUSL+YQ6m8WNga2NWrV7Fv3z67r2QBQOvWrdGqVStOeqJyUBQFPj4+du8GAewIEZVXWloacyiRjjGHUnmxsDUwR61kAf+sZnGbBlHZmc9Y7giSJEEIga1btzrk/YiMbuvWrcyhRDrGHErlxcLWwGRZho+PD3x9fR3yfpIkYd++fUhLS3PI+xEZ2dWrV7F//36HJWVfX192hIjKwdwNYg4l0h/mUKoIFrYGZr72nqOYTCauZhGVkSO7QcA/1+JjR4iobGRZZg4l0inmUKoIFrYGdfXqVSQlJTlswgOFq1k+Pj5czSIqA0VR4Ovr67BuEFDYEdq/fz+uXr3qsPckMiLmUCJ9Yw6limBha1CxsbEOXckyM5lMTMpEZeDoHRXAP8cI8cyrRKVzdDfIjDmUqGyYQ6kiWNgalKIoaN26NXx8fBz6vlzNIrqzK1euOPTYIDPz6ja3UhGVTpZl5lAinWIOpYpiYWtQjjxTXFE8axzRnZnnhxZzlB0hojvTohsEMIcSlQVzKFUUC1sDunz5MpKTkzVJyubriXE1i6hksiyjTZs2aNWqlcPf29wRunLlisPfm8gILl++7PDja82YQ4nujDmUKoqFrQFpuZJlfl+uZhGVTFEUTecnAHaEiErAHEqkb8yhVFEsbA1IlmX4+fmhZcuWmry/yWRCUlISV7OIrNByRwUAtGrVCm3atOEXZ6ISKIrCHEqkU8yhVBksbA1Iy5Us4J/VrNjYWM3GQKRX5nmh9RzlVkci67Q6R4UZcyhRyZhDqTJY2BrMpUuXcODAAc1WsgCgZcuW8PPz42oWkRWKosDf3x8tWrTQbAwmkwnJycm4fPmyZmMg0iPmUCJ9Yw6lymBhazDmlazIyEhNx8HVLCLrtO4GAewIEZVED90g8/szhxIVxxxKlcHC1mBkWdZ8JQsoXM06cOAALl26pOk4iPTk4sWLOHjwoKbdIABo0aIF/P392REiuo2iKGjbti3uuusuTcfBHEpUHHMoVRYLW4PR6tp7tzN3jHnWOKJ/mOeD1jsqAJ55lcgarc9RYcYcSlQccyhVFgtbA7l48SIOHTqki6RsXs3iViqif8iyrItuEFCYlNkRIvqHuRvEHEqkT8yhVFksbA3EvHKkh6QMFG6l4moW0T/0sqMC4DFCRLfTy/G1ZsyhRJaYQ6myWNgaiKIoCAgIQPPmzbUeCoDCSX/w4EFcvHhR66EQae7ChQu62VEBAHfddRfatm3LjhDR/5NlmTmUSKeYQ8kWWNgaiB7OFFcUV7OI/qG3bhDAjhBRUXrqBgHMoURFMYeSLbCwNYjz58/j8OHDukrKzZs3R0BAAFeziFC48NSuXTs0a9ZM66GoJEnCoUOHcOHCBa2HQqSp8+fPIyUlRVdfmplDif7BHEq2wMLWIPRy/drb8axxRIX0crbVotgRIirEHEqkb8yhZAssbA1ClmW0b99eVytZQOE2jZSUFK5mUZWmxx0VANCsWTO0a9eOX5ypylMUhTmUSKeYQ8lWWNgahB5XsoB/Vr856akqM//711s3CChcceZWR6rq9HaOCjPmUCLmULIdFrYGcPbsWRw5ckR3K1lA4WpW+/btmZSpSlMUBR06dEDTpk21HkoxJpMJhw8fxvnz57UeCpEmzp07xxxKpGPMoWQrLGwNQK/HBplxNYuqOr12gwB2hIj03A0CmEOJmEPJVljYGoAsy+jQoQOaNGmi9VCsMplMOHLkCM6dO6f1UIgc7uzZszh69Kguu0EA0LRpU3To0IFJmaosRVHQsWNH5lAiHWIOJVtiYWsAerv23u3Mq1k8axxVRXrfUQHwzKtUten1HBVmzKFUlTGHki2xsNW5M2fO4M8//9R1Um7SpAk6dOjArVRUJcmyjI4dO6Jx48ZaD6VEkiThyJEjOHv2rNZDIXIoczeIOZRIn5hDyZZY2Oqc3o8NMjOZTFzNoipJ7zsqAF6Lj6ou5lAifWMOJVtiYatziqKgU6dOul7JAgon/dGjR7maRVWKEXZUAEDjxo3RsWNHdoSoypFlmTmUSKeYQ8nWWNjqnJ7PFFcUzxpHVZFRukEAO0JUNRmhGwQwh1LVxBxKtsbCVsf+/vtvHD9+3BBJuXHjxujUqRMnPVUpiqKgc+fOaNSokdZDuSNJkvDnn3/izJkzWg+FyCFOnz6NY8eOGWJxmDmUqiLmULI1FrY6ZoQzxRXFa/FRVWOUHRUAO0JU9RipGwQwh1LVwxxKtsbCVsdkWUaXLl3QsGFDrYdSJiaTCceOHcPp06e1HgqR3RlpRwUANGrUCJ07d2ZSpipDURTmUCKdYg4le2Bhq2N6v/be7biaRVWJ+d95RESEtgMpB3aEqCoxUjcIYA6lqoU5lOyBha1OnTp1CidOnDDMShYANGzYEF26dGFSpipBURR07drVMN0goLAjdPz4cfz9999aD4XIrphDifSNOZTsgYWtThlxJQvgahZVHUbrBgH//D7hF2dydoqiwMXFhTmUSKeYQ8keWNjqlCzL6Nq1Kxo0aKD1UMrFZDLhxIkTOHXqlNZDIbKbv/76CydPnjRUNwgo7Ah17dqVSZmcnrkbxBxKpD/MoWQvLGx1yijX3rudeTXLfEZnImcUGxtryG4QUNgRYlImZ2e0c1SYMYdSVcAcSvbCwlaHUlNTkZqaasik3KBBA3Tt2pVbqcipmXdUeHt7az2UcpMkiR0hcmrmbhBzKJE+MYeSvbCw1SGjHhtkZjKZuJpFTs2oOyqAwjOvuri4cI6S02IOJdI35lCyFxa2OqQoCu6++25DrmQBhatZJ0+exF9//aX1UIhszsg7KgDA29ubHSFyarIsM4cS6RRzKNkTC1udEUIY8kxxRUVERHA1i5yW0btBADtC5NyM3A0CmEPJuTGHkj2xsNWZ1NRUnDp1ytBJ2dvbG3fffTcnPTklRVHQrVs3eHl5aT2UCpMkSV01J3Imqamp+Ouvvwy9OMwcSs6MOZTsiYWtzphXssLDw7UeSqXwWnzkjJxhRwXAjhA5L1mWmUOJdIo5lOyNha3OyLKM7t27G3olCyjcpvHXX39xNYucijPsqAAALy8vdOvWjUmZnI6iKMyhRDrFHEr2xsJWR4QQhr323u3Cw8Ph4uLCFWdyKrIsw9XV1fDdIOCfjpAQQuuhENmEs3SDAOZQck7MoWRvLGx15MSJE/j7778Nv5IFFK5mde/enatZ5FTM3aD69etrPZRKM5lMOHXqFDtC5DROnjzJHEqkY8yhZG8sbHVEURS4uroiLCxM66HYhCRJUBSFq1nkFJxpRwVQ2BFydXXlF2dyGuYc6gzdIIA5lJwLcyg5AgtbHTEfX+sMK1nAP6tZJ0+e1HooRJXmTDsqAKB+/fro3r07tzqS05BlGT169EC9evW0HopNMIeSM2EOJUdgYasT5pUsZ5nwABAWFgZXV1dOenIKzrajAuAxQuQ8nK0bBDCHknNhDiVHYGGrE8ePH8eZM2ecKimbV7O4TYOcgbN1g4DCpHz69GmcOHFC66EQVcrx48dx+vRp5lAinWIOJUdgYasTznSmuKJMJhOPESLDc8YdFQCPESLn4WzH15oxh5IzYA4lR2FhqxOKoqBnz56oW7eu1kOxKfNq1vHjx7UeClGFHTt2zOl2VABAvXr10KNHD251JMOTZZk5lEinmEPJUVjY6oAzXXvvdlzNImegKArc3Nyc6tggM3aEyOictRsEMIeSc2AOJUdhYasDf/75J86dO+eUSblu3bro2bMnkzIZmrPuqAAKO0JnzpxhR4gM69ixYzh79qxTLg4zh5IzYA4lR2FhqwPOvJIF8KxxZGzOvKMCKDzzqpubG7dSkWHJsswcSqRTzKHkSCxsdUCWZQQGBqJOnTpaD8UuTCYTzp49i2PHjmk9FKJyc+YdFQA7QmR8iqIwhxLpFHMoORILW40547X3bsfVLDIyczcoNDRU66HYDTtCZFTO3g0CmEPJ2JhDyZFY2GrsyJEjOH/+vFMn5Tp16iAwMJCrWWRIiqKgV69eTtsNAgo7QufOncOff/6p9VCIyuXo0aM4f/6803aDAOZQMjbmUHIkFrYac/bja824mkVGVBW6QQAQGhrKjhAZkizLqFatmlN3gwDmUDIm5lByNBa2GpNlGb169ULt2rW1HopdmUwmnD9/HkePHtV6KERlduTIEVy4cMGpu0FAYUeoV69e7AiR4Zi7QcyhRPrDHEqOxsJWQ8587b3bmVezOOnJSBRFQbVq1RASEqL1UOxOkiRei48MpSqco8KMOZSMiDmUHI2FrYYOHz6MixcvVomkXLt2bfTq1YvbNMhQqsqOCqAwKZ8/fx5HjhzReihEZXL48GFcuHCBOZRIp5hDydFY2GqoqhwbZGYymbiaRYZRlXZUAIUdoWrVqrEjRIZh7gYxhxLpD3MoaYGFrYYURUHv3r1Rq1YtrYfiEJIk4cKFCzh8+LDWQyG6o5SUlCqzowJgR4iMR5Zl5lAinWIOJS2wsNVIVTo2yIyrWWQkiqKgevXqVeLYIDN2hMgoqlo3CGAOJWNhDiUtsLDVyKFDh3Dp0qUqlZRr1aqF3r17MymTIVS1HRVAYUfo4sWL7AiR7qWkpODSpUtVanGYOZSMhDmUtMDCViNVcSUL4FnjyBiq4o4KAAgJCUH16tW5lYp0T5Zl5lAinWIOZQ7VCgtbjciyjD59+sDT01ProTiUyWTCxYsXkZKSovVQiEpUFXdUAOwIkXEoisIcSqRTzKGK1kOpsljYaqCgoACxsbFVbiUL4GoWGYO5GxQcHKz1UByOHSHSu4KCgirZDQKYQ8kYmEOZQ7XCwlYDBw8exOXLl6tkUvb09ESfPn24mkW6pigKgoKCqlw3CCjsCF26dAmHDh3SeihEVh06dAiXL1+uct0ggDmUjIE5lDlUKyxsNaAoCmrUqFElV7KAf1azCgoKtB4KUTFVuRsEAMHBwewIka7JsowaNWogKChI66FogjmU9Iw5lDlUSyxsNVBVj681kyQJly9f5moW6dLBgwdx5cqVKpuU2REivauqx9eaMYeSnjGHModqiYWtg5mPr62KW6jMgoODUaNGDU560qWqvqMCKNxKFRsby44Q6Q5zKHMo6RtzKHOolljYOtiBAwdw9erVKruSBfyzmsVtGqRHsiwjKCgINWvW1HoomjF3hA4ePKj1UIgsHDhwoEp3gwDmUNI35lDmUC2xsHUwWZbh7u5epVeyAK5mkT6xG1SIHSHSK0VRmEPBHEr6xBxaiDlUOyxsHcx8pjgPDw+th6IpSZJw5coVHDhwQOuhEKmSk5Or/I4KAKhZsyaCgoLYESLdMXeDmEOZQ0l/mEMLMYdqh4WtA1Xl69feLjg4GO7u7lzNIl0xd4Oq6tlWi2JHiPSG3aB/MIeSHjGH/oM5VBssbB0oKSkJaWlpTMoAPDw8uJpFuiPLMoKDg6t8Nwgo7AhdvXoVycnJWg+FCMA/OZSLw8yhpE/Mof9gDtUGC1sHMq9k9enTR+uh6IIkSVzNIt0oKCjA1q1b+aX5/wUFBbEjRLrCHGqJOZT0hDnUEnOoNljYOpAsywgJCeFK1v8zmUxIS0vjahbpAndUWPLw8EBwcDCTMumGoijMoUUwh5KeMIdaYg7VBgtbB8nPz+dK1m369OkDd3d3bqUiXZBlGR4eHuwGFcGOEOlFfn4+z1FxG+ZQ0hPm0OKYQx2Pha2D7N+/H+np6UzKRXh4eCAkJISrWaQL5m6Qu7u71kPRDXNHKCkpSeuhUBWXlJSE9PR0doOKYA4lPWEOLY451PFY2DqIoihcybLCvJqVn5+v9VCoCmM3yLrevXvDw8ODHSHSnLkb1Lt3b62HoivMoaQHzKHWMYc6HgtbBzEfX8uVLEuSJCE9PZ2rWaSp/fv349q1a0zKt+ExQqQX7AZZxxxKesAcah1zqOOxsHUA8/G13EJVXJ8+fbiaRZqTZRk1a9ZkN8gK87X42BEirTCHlow5lPSAObRkzKGOxcLWAfbt24fr169zJcsKd3d3HiNEmmM3qGSSJOHatWvYv3+/1kOhKmrfvn3sBpWAOZT0gDm0ZMyhjsXC1gG4klU6k8mErVu3cjWLNMFuUOl69+6NmjVr8oszaUZRFObQUjCHkpaYQ0vHHOpYLGwdQFEUhIaGokaNGloPRZfMq1n79u3TeihUBf3xxx/cUVEKc0eIWx1JK7IsM4eWgjmUtMQcWjrmUMdiYWtneXl5vH7tHXA1i7SkKAo8PT3Rq1cvrYeiW+wIkVby8vKwbds2doNKwRxKWmIOvTPmUMdhYWtnf/zxB27cuMGkXIoaNWogNDSUq1mkCXaD7kySJFy/fh1//PGH1kOhKobdoDtjDiUtMYfeGXOo47CwtTPzSlZgYKDWQ9E1SZKwbds25OXlaT0UqkLM3SB+aS5dr1694OnpyY4QORxzaNkwh5IWmEPLhjnUcVjY2pksywgLC+NK1h2YTCZcv36dxwiRQ3FHRdmYO0JMyuRoiqIwh5YBcyhpgTm0bJhDHYeFrR1xJavsAgMD4enpya1U5FCyLKNWrVrsBpWBJEnYunUrO0LkMMyhZcccSlpgDi075lDHYGFrRwkJCcjIyGBSLoMaNWogLCyMq1nkUOZuUPXq1bUeiu6ZTCbcuHGDxwiRwyQmJrIbVEbMoaQF5tCyYw51DBa2dqQoCleyyoHHCJEj5ebmshtUDuwIkaOZu0E9e/bUeiiGwBxKjsQcWj7MoY7BwtaOzMfXciWrbCRJwo0bN5CYmKj1UKgKSExM5I6KcqhevTo7QuRQ7AaVD3MoORJzaPkwhzoGC1s7yc3NRVxcHLdQlUNgYCBq1arF1SxyCFmWUbt2bXaDysFkMmHbtm3Izc3Veijk5MzdIObQsmMOJUdiDi0/5lD7Y2FrJwkJCbh58yZXssqBq1nkSOwGlZ8kScjIyGBHiOyOObT8mEPJkZhDy4851P5Y2NoJV7IqxmQyIS4ujqtZZFfcUVExPXv2RO3atfnFmexOURTm0ApgDiVHYA6tGOZQ+2NhayeKoiA8PBzVqlXTeiiGYl7NSkhI0Hoo5MT27t3LblAFmDtC3OpI9ibLMnNoBTCHkiMwh1YMc6j9sbC1g5ycHMTFxXHCV0CPHj24mkV2pygK6tSpgx49emg9FMORJIkdIbIrczeIObT8mEPJEZhDK4451L5Y2NrB3r17kZmZyS0aFVC9enWEh4dzNYvsit2gijOZTLh58yb27t2r9VDISe3Zs4c5tIKYQ8kRmEMrjjnUvljY2oF5Jat79+5aD8WQuJpF9pSTk4Pt27ezG1RBPXr0QJ06ddgRIrthDq0c5lCyJ+bQymEOtS8WtnYgyzIiIiK4klVBJpMJmZmZ2LNnj9ZDISfEblDlVKtWjR0hsivm0MphDiV7Yg6tHOZQ+2Jha2Ncyaq87t27czWL7EZRFNStWxfdunXTeiiGJUkStm/fjpycHK2HQk6GObTymEPJnphDK4851H5Y2NrY7t27cevWLSblSqhWrRoiIiKYlMkuFEVhN6iSzB0hHiNEtrZnzx7cunWL3aBKYA4le2IOrTzmUPthYWtj5pUsHhtUOVzNInvIzs5mN8gGunXrhrp163IrFdmcLMvsBtkAcyjZA3OobTCH2g8LWxszHxvk5uam9VAMTZIkHiNENmfuBjEpV475GCF2hMjWzN0g5tDKYQ4le2AOtQ3mUPthYWtD2dnZ2LFjB7dQ2UD37t25mkU2J8sy6tWrx26QDZhMJmzfvh3Z2dlaD4WchLkbxBxaecyhZA/MobbDHGofLGxtaPfu3cjKyuJKlg24ubnxGCGyOXaDbEeSJNy6dYsdIbIZ5lDbYQ4le2AOtR3mUPtgYWtDsiyjfv36uPvuu7UeilPgahbZEndU2Fa3bt1Qr149doTIZphDbYs5lGyJOdS2mEPtg4WtDXEly7YkSUJWVhZ2796t9VDICezatYvdIBtiR4hsjTnUtphDyZaYQ22LOdQ+WNjaSFZWFnbs2MEJb0N333036tevz0lPNqEoCurXr4+uXbtqPRSnIUkSduzYwY4QVVpWVhZ27tzJHGpDzKFkS8yhtsccanssbG1k165dyM7O5hYNGzKvZnGbBtmCLMuIjIxkN8iGTCYTsrKysGvXLq2HQgZn7gYxh9oOcyjZEnOo7TGH2h4LWxtRFAVeXl5cybIxSZKwc+dOZGVlaT0UMjB2g+yja9eu7AiRTTCH2gdzKNkCc6h9MIfaHgtbGzGvZLm68kdqS1zNIluIj4/njgo7cHNzQ2RkJDtCVGnMofbBHEq2wBxqH8yhtscMYgNZWVmIj4/nSpYddO3aFV5eXlzNokpRFAXe3t7o0qWL1kNxOuwIUWUxh9oPcyjZAnOo/TCH2hYLWxvYuXMnsrOzmZTtwNXVFZGRkUzKVCmKorAbZCcmkwnZ2dnsCFGFsRtkP8yhZAvMofbDHGpb/BdqA1zJsi+uZlFl3Lp1i8cG2VGXLl3g5eXFrVRUYbIsw9vbG507d9Z6KE6JOZQqgznUvphDbYuFrQ3w2CD7kiQJ2dnZiI+P13ooZEDx8fHIyclhUrYTdoSostgNsi/mUKoM5lD7Yg61LWaRSsrMzMSuXbu4hcqOunTpAm9vb65mUYXIsowGDRqwG2RHJpMJO3fuxK1bt7QeChnMrVu3EB8fzxxqR8yhVBnMofbHHGo7LGwriStZ9sfVLKoMdoPsT5Ik5OTksCNE5bZz507mUDtjDqXKYA61P+ZQ2+G/0kqSZRkNGzZEp06dtB6KUzOZTIiPj+dqFpVLZmYmu0EO0LlzZzRo0IAdISo35lDHYA6limAOdQzmUNthYVtJXMlyDPNq1s6dO7UeChnIzp07kZuby26QnbEjRBXFHOoYzKFUEcyhjsEcajvMJJVgPr6WE97+OnXqhAYNGnDSU7koioKGDRuiY8eOWg/F6UmShF27diEzM1ProZBBMIc6DnMoVQRzqOMwh9oGC9tK2LFjB3Jzc7lFwwFcXV0hSRK3aVC5yLIMSZLYDXIAk8nEjhCVC3Oo4zCHUkUwhzoOc6ht8F9qJSiKgkaNGnEly0G4mkXlcfPmTezevZvdIAfp2LEjGjZsyI4QlRlzqGMxh1J5MIc6FnOobbCwrQTzSpaLi4vWQ6kSTCYTcnNzsWPHDq2HQgbAbpBjsSNE5cUc6ljMoVQezKGOxRxqGyxsK4grWY7H1SwqD3M3qEOHDloPpcqQJAm7d+/GzZs3tR4K6RxzqOMxh1J5MIc6HnNo5bGwraDt27cjLy+PSdmBXFxcuJpFZcZukONJksSOEJUJc6jjMYdSeTCHOh5zaOWxsK0gRVHQuHFjrmQ5mMlk4moW3VFGRgb27NnDLVQO1rFjRzRq1IgdIboj5lBtMIdSWTCHaoM5tPJY2FYQV7K0IUkS8vLyuJpFpdqxYwe7QRowd4SYlOlOFEVhDtUAcyiVBXOoNphDK4+FbQVwJUs7HTp0QOPGjbmVikolyzKaNGmC9u3baz2UKsfcEcrIyNB6KKRTzKHaYQ6lsmAO1Q5zaOWwsK2A7du3Iz8/nytZGuBqFpUFu0HaYUeI7oTH12qHOZTKgjlUO8yhlcPCtgJkWUbTpk3Rrl07rYdSJZlMJuzZs4erWWTVjRs32A3SUPv27dGkSRN2hKhEzKHaYg6l0jCHaos5tHJY2FYAV7K0ZV7N2r59u9ZDIR3ijgptsSNEd8Icqi3mUCoNc6i2mEMrh4VtOd24cQN79+7lhNdQu3bt0LRpU65mkVXmblBAQIDWQ6myJEnCnj17cOPGDa2HQjrDHKo95lAqDXOo9phDK46FbTnFxcUhPz+fWzQ0xNUsKo2iKDCZTOwGachkMiE/P58dISqGOVR7zKFUGuZQ7TGHVhwL23JSFAXNmjVD27ZttR5KlSZJEvbu3cvVLLJw/fp1JCQksBuksYCAADRt2pRfnKkY5lB9YA4la5hD9YE5tOJY2JaTLMtcydIB82pWXFyc1kMhHWE3SB9cXFxgMpm41ZGKYQ7VB+ZQsoY5VB+YQyuOhW05cCVLP9q2bYtmzZpxNYssKIqC5s2bw9/fX+uhVHmSJCEhIQHXr1/XeiikE8yh+sEcStYwh+oHc2jFsLAth23btqGgoIBJWQfMxwhxNYuKkmWZZ1vVCUmS2BEiC8yh+sEcStYwh+oHc2jFsLAtB0VRcNddd3ElSydMJhNXs0h17do1JCYmcguVTrRt2xbNmzdnR4hUzKH6whxKRTGH6gtzaMWwsC0HrmTpiyRJKCgowLZt27QeCukAu0H6wo4Q3Y45VF+YQ6ko5lB9YQ6tGBa2ZZSeno4//viDK1k64u/vj7vuuourWQSgsBvUokUL+Pn5aT0U+n8mkwmJiYm4du2a1kMhjTGH6g9zKBXFHKo/zKHlx8K2jOLi4riSpTO8Fh8VpSgKu0E6Y+4I8RghYg7VH+ZQKoo5VH+YQ8uPhe0d/PXXX9i3bx9kWUaLFi3Qpk0brYdERZhXsy5fvowNGzZoPRzSwIYNG3Dp0iV2g3TIz88PLVq0gCzL2LdvH06dOqX1kMjBiubQli1bMofqDHMoMYfqF3No+bGwvYNFixZhxIgRUBQFkZGR+Pzzz7klQCc2bNiAJk2aoKCgAG+88QYGDhyI7OxsrYdFDpSdnY2BAwdi4cKFKCgoQJMmTfjlTCeuXbuGzz//HBEREVAUBcOHD8eiRYu0HhY5WNEcGhERwRyqI8yhxByqX8yhFcPC9g66dOmCY8eOITExEWfPnsW0adNw4cIFrYdFAFauXInx48ejSZMm+P3339GuXTu4u7trPSxyIHd3dwQEBOD3339H06ZNMW7cOKxcuVLrYRGACxcuYNq0aTh37hwSExNx/PhxdOnSRethkYMxh+oXcygxh+oXc2jFsLC9g6CgIPXPsizjk08+QUBAgIYjIrP3338fTZs2RWZmJg4fPmwRK6o6goKCcPjwYWRkZKBp06Z4//33tR4SAQgICMDHH38MWZYhhAAAztEqiDlUv5hDCWAO1Svm0IphYXsHPj4+8PT0BAA899xzmDx5ssYjIrP69evjl19+AVC4nebuu+/WeESkhbvvvhvZ2dlwdXXFL7/8gvr162s9JPp/U6ZMwcyZMwEAtWrVQqtWrTQeETkac6h+MYcSwByqZ8yh5ecizMsAVKLw8HDcunULu3fvhqsr1wL0Zt26dRg5ciS2bduGnj17aj0ccrCEhASEh4fj+++/x8CBA7UeDt2moKAAvXr1gqenJ6+XWUUxh+obc2jVxhyqb8yh5cPCloiIiIiIiAyNS6dERERERERkaCxsiYiIiIiIyNCq2eqFTp06hcuXL9vq5aiSGjZsWOpB5oyXczl37hwAoFmzZhqPhGyB8XQ+2dnZvJSKEymaY5lPjcscR8bQuTCHOp871TVmNilsT506hQ4dOiAzM9MWL0c24OnpiZSUFKv/CBgv5+Pq6oqCggKth0E2wng6Hzc3N+Tn52s9DLIRc44FwHxqYJ6enti8eTNiYmIYQyfCHOp8SqtrirJJYXv58mVkZmZixYoV6NChgy1ekiohJSUFDz30EC5fvmz1HwDj5VzM8WY8nQPj6XzWr1+PV155hTF1EkVzLADmU4Myx/HEiROMoRNhDnU+d6prirLZVmSgcNWyR48etnxJsiPGy7kwns6F8XQeRTt7jKlzYmyNjzF0Loxn1cSTRxEREREREZGhVYnCdu7cuVixYoXWwygTIQSeffZZhIeHIyYmBqdPny72mPT0dAwZMgTh4eGYMGECcnJyNBip/RgpXkePHkWPHj1Qu3ZtxMXFqbfPnTsXffr0QWhoKJ588klYu1x0UlISwsLCEBISgnfeeQdA4fHPkiSp/1WvXh1paWnYuXMnQkJCEBkZiaioKJw4ccJhn7GynCGeZZlzw4YNQ2RkJAIDA/Hvf//b4r4rV67Ay8tL/TmkpqbCy8tLjfPPP/9s3w9mQ1U5nhcuXEBISAgkSUKfPn3w+++/Wzxny5YtcHFxsfp7W6+cIZ5Hjx6FJEkwmUx47rnnrD63devW6nybN28egMJ8O3nyZEREROCee+7BmTNnAADz5s1TH+vr64v/+Z//sf+HsxMjxRcAvvnmG0RHR8NkMuG9994rdn9Jsf7555/Rp08fhIeH45tvvgEA7N69W41jUFAQGjRo4LDPYUtGimFJc9Ts1Vdfhb+/v9XnSpKE4OBgSJKEadOmASicozNmzEBwcDB69eql/hwWLVqkxrZ9+/YYNmyY/T6UjRkpnmYl5bYlS5agevXqVp8zZcoUBAUFISgoCAsXLlRvHzRoEEJDQ9GnTx98+eWX6u3m77i9evXCypUrKzxWQxe2jjoRR9H3EUJYLVJKe055/Pbbb7h8+TK2bduG559/HrNmzSr2mDfffBNDhgzBtm3b0Lx5c3z11VcVei9Hc8Z4tWjRAr///juGDx9ucfuYMWOwa9cubN++HefPn8eWLVuKPXf69On44osvEBcXhzVr1uD48eNo1aoVFEWBoihYsGAB+vbtCy8vL/j4+OC3335DbGwsZs6ciTlz5lRovLZUleJZljm3cuVKxMbGIj4+Hh9//DFu3Lih3jd//nyEhYVZPL5nz55qrAcPHlyh8doS42nJWjwbNmyIbdu2QVEUrFy5Ei+++KL6+IKCArz99tsIDAys0FhtrSrF8/nnn8fChQshyzLS0tIgy3Kx57q5uanz7dVXXwUArFmzBm5ubti6dStmz56Nl19+GUDhl2/zYwMCAvDggw9WaLz25IzxTUlJwdq1a/Hbb79BlmU89dRTxR5jLdYFBQV47rnnsHnzZmzZsgXvvvsubty4gd69e6txfPLJJ3UXR2eMYUlzFADOnDmDP//8s9Tnf//991AUBR9//DEA4ODBgzh48CB27txpMXenTJmixjYiIkIXsXXGeAIl57bMzEz89NNPJR7zOnPmTMTHx2PHjh3qd1wAePfdd7F9+3bExsbiX//6F7KysgAAiqIgNjYWv//+u/q7uCLsVtimpqYiNDQUJpMJkiQhLS0NFy5cQHR0NPr164dp06YhJiYGADBhwgR1ZUdRFEyaNAkA8N577yEqKgqBgYGYPXu2+to+Pj6YPn06+vXrh2vXrmHkyJGIioqCyWTC0aNHAQCrVq1Ct27dMHjwYCQkJJQ4ztzcXEyePBlRUVEICwvDzp071TE9/vjjuO+++7Bx40b4+/tj1qxZiI6OxunTpzFq1ChERkYiNDRUff3bn1MRsixj6NChAIC+ffti9+7dpT7m/vvvt5rEy4vxqli8PD094eXlVez2gIAA9c/u7u5wc3OzuD87OxvXr19H27Zt4erqikGDBiE2NtbiMcuWLcO4ceMAAM2bN0etWrVKfL3bMZ62jWdZ5lyNGjUAFP6yb9WqFTw9PQEAx44dw5UrV9CzZ0+Lx+/fvx/h4eF46KGHcOnSpVLHxXjqI55ubm7q3EtPT0fXrl3Vx69YsQKDBw9W52lpGE/bxvPo0aPql67AwECr8RRCwGQyoX///khMTCzT886ePYvTp0+jd+/e5Ron41ux+H7//feoV68e7rnnHgwcOBBHjhwp9hhrMbt8+TIaNWqEOnXqoHr16mjTpg327Nlj8byi+bQsGEPbzlGgsFNZ9OdwOxcXF4waNQomkwmbN28GUPjdp0aNGsjNzcWNGzfg7e1t8ZysrCxs2rTpjovDjGfF4gmUnNvefvttPPHEE3BxcbH6PPP3YFdXV1SrVk3Nnebba9SoAVdXV/X55px748YNdOrUqcLjhbCBhIQEAUAkJCSoty1ZskTMmTNH/XtBQYF46qmnxIoVK9T7o6OjhRBCPPzww2Lbtm1CCCFkWRaPPvqoEEKIjIwM9blBQUEiNTVVCCFEtWrVxMmTJ4UQQrzwwgti5cqVQgghDhw4IIYMGSLy8/NFQECASE9PF/n5+SIyMlIsX77c6tg/+eQT8cYbbwghhLh48aIICgpSxzR//nz1cT4+PiIuLk4IIcR//vMf9bMdPnxY9OnTx+pzinr88cdFZGSkxX8DBw4s9rjJkycLWZbVv7dt27bYYwICAkRBQYEQQoijR4+KAQMGWNxvLR53up/xslTWeJkV/ZkUJcuykCRJ5OfnW9x+5swZERkZqf590aJF4s0331T/np2dLVq1aiUyMzMtnpeRkSGCg4PFvn371NsYT/vH805zzmzw4MGiUaNG4tVXX1VvGz16tDh27JiYM2eO+nPIysoS169fF0IUxmL8+PHq4xlPfcfzxIkTIjQ0VDRs2FCsXbtWCCFEZmamiI6OFrm5uSIyMlL8/fffFq+zYsUKi5gynpYqG88RI0aItWvXioKCAjF06FAxffr0Ys+5dOmSEEKI5ORk0aFDByGEEL/88osYPny4KCgoEKtWrRK1atWyeM6bb74p/vWvfxV7raJzlPPVdvGdMmWKuP/++0V+fr7YvXu3RY40sxZr82c+ffq0SE9PF61atRLff/+9+pxz586Jdu3aFXstc+xun59CMIa3q+wc3b9/v5g0aZIQQgg/Pz+rzzHP0dOnT4t27dqJ9PR0UVBQIB5//HHh6+srGjVqJH766SeL53z33Xfqz9uMc9J28Swpt507d04MHjxYCFFyPM2WL18uRo8eXez2+fPni5deesnivcLDw4W3t7dYtGiRxWPvVNcUZdOzIhc1cuRILFiwAGPHjoWPjw/mzp2LI0eO4IknngBQuJf666+/BgCLal8Uaan/+OOPWLx4MVxcXHDy5EmcPn0aPj4+aNGiBXx9fQEAycnJiI2Nxaeffqq+1qVLl9C4cWPUq1cPABAUFFTiOJOTk7Fjxw78+uuvAApX4c2Kbh10c3NDSEgIAODw4cPqfv527drhypUrVp9TlHlbxZ00aNAAaWlp6s+iWrXiIfL29kZ6ejq8vLyQnp5uk+NGGC9LZY1XaRITE/HSSy9h3bp1cHW13BxhjqHZ7XFcu3YtoqKiULNmTfW27OxsDBs2DC+99BLuvvvuUt+b8bRU2XiWdc6tWbMGN2/eVLdGmR/r5+dn8Th3d3e4u7sDAMaOHYt333231PdnPC1pFc+OHTuidevWiIuLw4kTJxAVFYVBgwbhP//5D6ZOnWr197U1jKelysbznXfewYwZM/Dee++hTZs2aN68ebHHNGzYEADQuXNn1K9fH5cuXcKAAQOwY8cOSJKEXr16oX379hbPWbFiBdasWVPu8TC+lsoaX29vb3Tv3h2urq7o1auXesxzUdZi7erqis8++wwPPfQQ6tSpg27duln8G/jqq68wevToMo3BjDG0VNk5+uqrr+LDDz8s9THmOXrXXXehR48eOHr0KK5evYozZ87g2LFjuHbtGsLCwjBgwAA1fy5btgwzZ8684/sznpbKGs+Sctuduu9mGzZswLJly4qdR2Tx4sU4ePCgxbHGNWvWxNatW3H58mX06tULI0eOVH9m5WG3wtbV1RULFiwAAEycOBEbN25EQEAAdu3aBT8/P8THx6uP9fb2xqlTpwDAYvvIK6+8gsOHD8Pd3R2hoaHqP7Ci2zA7deqE4OBgdVtZTk4O3NzccOHCBVy/fh116tTB7t270blzZ6vj7NSpE/z9/fHMM8+ozzcr+j4uLi7qP/b27dtj27ZtiImJwZEjRyy+FJW0RXTatGk4dOiQxW21a9fGunXrLG6TJAnffPMNhg4dii1btlg9Xst8spmHH34YP//8MyRJsvqe5cF4WSprvEqSkpKCKVOmYPXq1Va/NHt4eKBOnTo4ceIEWrdujfXr1+Pzzz9X71++fDmefPJJ9e95eXkYNWoURo8ejfvuu++O7894WqpsPO805woKCpCfn4/q1aujZs2a6n+bN29GUlIS7rnnHhw7dgy1atWCn58fOnbsqP7ClmXZYuu6NYynJa3imZ2drX6hqlu3LmrXrg2g8Diw2NhYLF68GElJSRg3bhzWrVtX4rZkxtNSZePZsmVLrFmzBkIIjB8/Xv28ZtnZ2RBCwMPDA+fOncOlS5fUcc2fPx8AsHHjRnUrHADs27cP9erVU7+wlgfja6ms8Y2OjlZP/HTixIli206BkmNtPpHQjRs3MGzYMPTq1Ut9zooVK/DDDz9YHVtJGENLlZ2jx48fV7f0njt3DtOnT8dHH32k3i+EwI0bN1C3bl1kZGRg//798PX1xdWrV+Ht7Q03NzfUqVMHubm56vGily5dQkpKCiIiIu74/oynpbLGs6TcduzYMbzyyisACuM5fPjwYnNs69atmD9/PtavXw8PDw/19m+//RarVq3C6tWr1aZPTk4OqlWrBldXV9SqVQseHh4WzykPuxW269atwwcffAA3Nze4u7sjLCwMgYGBGDNmDL744guLoE6aNAljxozB119/jdatW6u3P/DAAwgNDUX79u3VLxC3mzVrFqZOnYoPPvgAANC/f3+88MILeOONNxAREYGWLVta/eVoNnnyZMyYMQMmkwkA0L179zt2TyZPnowJEyYgIiICeXl56nuXpqyrI/369cOGDRsQHh4Od3d3fPHFFwCAX3/9FZcuXcK4cePw/PPP4+GHH8aSJUvg6+ur/uOqDMbLUlnjlZaWhhEjRuDQoUM4ePAg+vfvj/nz5+Pxxx/H9evX8dBDDwEAnn32WQwePBgLFy7EwIED0aVLF7z//vsYP348CgoKMHToUPVMgVeuXEFSUpLFl+0VK1bg999/R1paGr744gt07Nix1DEynpYqG8+S5pw5nr6+vuqCQ3Z2NkaNGoXWrVvjySefVBco5s6dC39/fwQHB2P16tWYN28e6tSpA3d3dyxatKjUcTGelrSKZ1xcHF5++WW4ubkhNzdXPWtr0VVnSZKwfPnyUo+1ZTwtVTaeX3/9tbowOGHCBHTo0AEA8PTTT2PWrFnIysrCkCFDUKtWLeTl5eGTTz6Bq6sr0tLSMHToULi5ucHHx8eio7R8+XL193d5Mb6Wyhrf6OhobNq0CZIkIScnR33tot9/Sor1888/jz179qBatWpYuHCheqbW5ORkeHp6Fts1cyeMoaXKztHk5GT1Mf7+/mpRa/6d2759e5hMJtSsWRM5OTmYPXs2GjVqhJiYGKxcuRKhoaHIycnBE088oZ6/4ptvvsHIkSNLPMazKMbTUlnjWVJuMx8DDRTG01zULl26FHfddRf69u2L8ePHo27duhgyZAiAwpM2duvWDePGjUO3bt3Qt29fAIVd95ycHEycOBFubm7Izs7GnDlz1EXkcrvjZuUyKM/eZ7OTJ0+q+9nJtipyjO2dMF76xXg6F8bT+Vg7hq80jKe+3ekY2zthfPWhtGNs74Qx1C/OSeeji2Ns9Wj8+PHq9gKgcLvBTz/9pOGIqDSMl3NhPJ0L4+lcGE/nxvgaH2PoXBhP+9CssPX19bVoZTvCsmXLHPp+zoTxci6Mp3NhPJ0L4+ncGF/jYwydC+PpPOx2HVsjMR/baG+fffYZevfujYiICIwePRrZ2dkOeV9n5qjYxcXFoUuXLvDw8MDp06fV22/duoWpU6ciJiYGkiSp192TJAnBwcGQJAnTpk1zyBidjaNiCwD//ve/ERMTA5PJhG+//RZA4ckVzCckadq0aZmOW6GSOSqeS5cuRevWrdXYmVfE//77b8TExCA8PLxMZ9GkO3PkHAWAJUuWqMdOAsDrr7+OiIgIhIaGYvz48cjNzXXoeJyZ1rGlytP6u+0PP/yADh06VPgkQFQyrWOrZyxsHSg6Ohrx8fHYunUrWrZsaXFQNulbly5dsHPnzmKnaZ83bx4GDx6MzZs3Q1EUtGvXTr3v+++/h6IoNrl0ENnPxo0bcf78eWzevBmyLOPBBx8EUHhyBUVRoCgKvL29MXz4cI1HSmX16KOPqrFr1aoVAOCFF17Ayy+/jG3btuHs2bOQZVnjUVJ5ZGZm4qefflLjCQDPPfcctm7diu3btwMANm3apNXwqBKsxZaMo6TvtpIk4Y8//kCLFi00HiFVlBHrFl0XtqmpqQgNDYXJZIIkSUhLS8PWrVthMpkQHh6OwYMH49atWwAKJ9AzzzyD/v37Q5IkfP311+jfvz+6deuGo0ePqo+ZPn06+vbti+joaFy8eNHi/XJzczF58mRERUUhLCwMO3fuBFB4OYDevXsjKirqjmcnK42/v796amt3d/cST8PtDJwtdvXq1bN6FrxNmzZBlmVIkoRnn30WeXl5AApPxT5q1CiYTCaHb2+xN2eL7bfffov8/HzExMRgxIgROH/+vMX98fHxaNmyJZo1a1bh99AzZ4snULjFKywsDLNmzUJBQQGAwutKR0VFAQDuv/9+py5snTGmb7/9Np544gmLM6CaL89TUFCAvLw8h3cZtVBVYuvMnC2GJX23bdiwYZXr1laV2Oqao89WVR5LliwRc+bMUf9eUFAgMjIy1L8///zzYunSpUIIISIjI8WqVauEEEI8+uij4umnnxZCCLF8+XLxwgsvqI/58ssv1deeOXOmEEIIPz8/IYQQn3zyiXjjjTeEEEJcvHhRBAUFCSGEaN++vfq++fn5xcY5dOhQERkZafHfo48+WuLnOnTokOjZs6fFZ7Ele5wVubycNXaRkZHi77//Vv9eo0YN8dNPPwkhhJgxY4ZYsmSJEEKIS5cuCSGEOH36tGjXrp1IT08v/QdWCY6IZ1HOFtt+/fqJJ598UgghxPfffy/GjRtncf+0adPE8uXLy/rjqTTGs3LxvHr1qsjLyxN5eXliwoQJ4r///a8QQoi2bduqj9m0aZOYNm1aeX5MlVKRs65WhrPF9Ny5c2Lw4MEW72n26quvCj8/P3HvvfeKmzdvlvVHVCmVPStyZVSl2NpbZc6KXBnOFkOzkr7bahVXR8WzqKoWW0dxmrMijxw5EgsWLMDYsWPh4+ODuXPn4uDBg5g9ezays7Nx4cIF1K1bV318z549AQAtWrRQr1nWokULxMbGqo8JCQlR/79q1SqL90tOTsaOHTvw66+/AgDS09MBAB9++CGmT5+OvLw8TJ06FWFhYRbPK89ZzFJTU/Hwww/ju+++K/U6h0bnjLGzxtvbG/feey8AYODAgVi/fj2AwpVKALjrrrvQo0cPHD161OKC8UbmbLEtGsNBgwZh3rx56n05OTn45Zdf8Oabb5bptYzI2eLp5eWl/nn06NFYs2YNHnnkEXXV2fyeRS9i72ycLaZz587F7Nmzrd732muvYe7cuZg+fTqWLl3q9Oc0qEqxdVbOFkOg6ny3vRPGVnu6LmxdXV2xYMECAMDEiROxceNGLF68GK+99hqCg4Px/PPPQwihPr7oNpaify76mPj4ePj7+yM+Pt7ieEgA6NSpE/z9/fHMM88AKPxSCwDBwcGIjo7GqVOnMHToUCQkJFg874EHHsDVq1ctbvP398fixYstbjt//jxGjBiBJUuWoE2bNuX+eRiJs8WuJNHR0di7dy/CwsKwa9cuBAQEQAiBGzduoG7dusjIyMD+/fvh6+tbptczAmeLrTmG99xzjxpDs/Xr1yMiIsIQv8wrytnimZ6ejvr16wMAtmzZor5/9+7dERsbi8jISPz888945JFHyv5DMhhni+mxY8fwyiuvAADOnTuH4cOH44cffkBWVhY8PDzg4uKCevXqwdPTs3w/KAOqKrF1Zs4Ww6r03fZOGFvt6bqwXbduHT744AO4ubnB3d0dYWFhyMjIwKOPPor27dujbt26FisfZZGYmIgvv/wS+fn5WLlypcV9kydPxowZM2AymQAUfhF69913MXToUGRlZSErKwvTp08v9pplXfl48cUXceHCBTz55JMAgDFjxmDKlCnlGr9ROFvsUlJS8MQTT2D//v0YPXo0HnzwQcyYMQMLFy7EpEmTkJWVhcaNG2PZsmXIy8uDyWRCzZo1kZOTg9mzZ6NRo0bl+qx65myxHT9+PKZOnQqTyQQhhMUv9uXLl+Oxxx4r12cxGmeL51tvvYXNmzejWrVqaNu2rdqBX7hwISZOnIicnBwEBgaq7++MnC2mRc9T4O/vrxY+06ZNw4kTJ5Cfn4+AgACL3RbOqqrE1pk5WwxL+m6rKArmz5+Ps2fPIiYmBo899hhGjBhRrs9lNFUltrrm6L3PWrr9+EhnpYdjbG2tqsSuIowYz6IYW0uMp/Nx9DF8tsaYWtLyGFtbq8qx1eoYW1uryjG0xuhzsijGtlB5YqrrsyITERERERER3YmutyLbmqIoWg+BKoixc16MrXNhPJ0PY+q8GFvjYwydF2Nbfk7TsU1NTUVMTIzD3m/u3Lno0KEDJEmCJEnqAdtJSUkICwtDSEgI3nnnHavPXbFiBYKDgxEcHOzU11IsCy3i1qdPH4SGhuLJJ59UD9CfMmUKgoKCEBQUhIULF1p97l9//YX7778fUVFR6ll0AaBmzZrqv4NFixYBAH799VeEhoZCkiRERUXh77//tv+H0wFHx/OHH35Ahw4dil0r78qVKxgzZgyio6MhSRKuX79ucb8QAjNmzEBwcDB69eqlXnR80aJFaizbt2+PYcOGASj5301VoJeYDho0CKGhoejTpw++/PJLq88t7XfrkSNHUL16dcTFxQEA1qxZgz59+iAyMhIDBw7ElStX7POBdMbR8Xz99dcRERGB0NBQjB8/Hrm5uQDKFk+gcC57eXmpcxQoPClYTEwMTCYTXnrpJQCFXwCbNWumzt/du3fb94PphF7mp1lERAQmTZpU7PYLFy4gJCQEkiShT58++P333wGUPA+XLl2K1q1bq/E8deqU/T6Ujjg6nmavvvqqxXWgb926halTpyImJgaSJOHIkSMWj09PT0d0dDTCwsIQFBSEDRs2WNx/+7zNycnByJEjER4ejt69e+O3336z/4fSIb18592wYQN69eqF8PBwjB49Wv29XJS177aG+D3r6L3P9nLy5EkRHR3tsPebM2eO1WtbhoWFiaNHj4r8/HwRHh4ujh07ZnF/WlqauPvuu8WtW7fEpUuXxN133231GlOVYaRjbB0dtyNHjqh/HjFihNi8ebPF7fn5+SIoKKhY3IQQYsCAAeL06dPFbrd2jbbs7Gz1z0WvPWYPVTmely5dErdu3SoWg3Hjxol9+/aV+Lzk5GQhSZIQQoiMjAzRunXrYo+ZPHmy+Pbbb4UQJf+7sQc9xVMI/cTUHAPzfbdu3bK4/06/W0eNGiViYmLEtm3bhBCFnysnJ0cIIcRHH30kZs+ebbfPpKdj+Bwdz6K/C8eNGyfWrVsnhLhzPM2efvppMWjQIDXfXr58WQwYMEBkZWVZPE6W5VKvw2hLejrGVi/zUwghfvrpJ3HfffdZjYP52tJCCHH8+HERGBgohCh5Hn7xxRfiX//6l70+hhBCn8fYOjqeQghx+vRpMWrUKIuYvvjii+KXX34p8TmZmZnqsZ+XLl0SAQEBFvffPm/Xrl0rJkyYIIQQ4tSpU6JHjx62/hgqredkafTynbdnz54iNTVVCFF4Dd2ff/652HOtzXFH/p4tymmOsZ05cyZ+/PFHAEBeXh66du2K3NxcvPzyy4iKikKPHj3wySefFHvehAkT1FV5RVHU1cMDBw4gJiYGUVFRGDFiBDIzMys1vjfffBNhYWH497//DQDIzs7G9evX0bZtW7i6umLQoEEW16ICgF27diEyMhIeHh5o2LAhmjdvjtTU1EqNQ2/0HLeil3Jxd3eHm5ubxe2urq6oVq2aervZqVOncPPmTfzP//wPIiMjsWTJEvW+8+fPIzIyEvfffz9OnDgBAKhRo4Z6f3p6Orp27VrhMWtNz/Fs2LBhsc5Bfn4+kpKS8OGHHyIyMhKvv/56sec1b94cNWrUQG5uLm7cuAFvb2+L+7OysrBp0yYMHjwYQMn/bozKaDEF/olBjRo14OrqanFpBKD0361bt26Fr68v7rrrLvXxvr6+qF69OgDjx1TP8TT/LiwoKEBeXp7aFbpTPIHCS8FcuXJFvdYjAPzyyy9o1KgR7r//fsTExCA+Pl69b9OmTQgLC8O0adMqnd+1pOd4ljQ/8/Ly8Mknn1g9AysAuLm5qXOsaE4sbR4uW7YMYWFhmDVrFgoKCio8Zq3pOZ6A9WsJb9q0CbIsQ5IkPPvss8jLy7O4v2bNmmjRooX656Lz19q89fPzQ3Z2NoQQSEtLQ+PGjSs1Zj3Rc3xL+u7SqVMnpKenQwiB9PR0q1fusPbdFtD/71ldF7YTJkxQtyht3LgRUVFRqF69OmbNmoUtW7Zg586d+Pe//221hW7NtGnT8N///hdbtmyxaK2b5eTkqO31ov9Zu3i4+dIvmzdvxq+//gpZltWtF2ZeXl7FtreV5TFGp+e4mSmKgtOnTyMiIsLi9hUrVqBly5bFrjt75swZJCQk4M0338TGjRvx+eef488//wRQuLUkNjYWTzzxBCZOnKg+Z9WqVQgMDMQnn3yC4ODgMn1WPTJCPIu6ePEikpKSMHXqVMiyjN27d2PLli0Wj/Hy8oKfnx8CAgLQtWtXzJo1y+L+tWvXIiYmptgXuJL+3RiN0WJa1BtvvIHhw4fD3d3d4vaSfrcKIbBgwQK88MILVl/v/Pnz+PDDD/V/CYNS6D2ec+bMQUBAANLT09GyZUuL+0qKJ1C4PXLOnDkWt505cwYpKSlYtWoVlixZov7O7dmzJ44ePYq4uDg0bNgQ//u//1umz6pHeo+nNZ9++ikeeughq3E0O3nyJMLCwtC/f38MHTrU4r7b5+GQIUOQkpKC2NhYnD17ttTt6nqn53gmJSUBKCx0ijpw4ABCQkKgKApyc3OxbNmyEsfz9NNP49lnn1X/bm3etmnTBpmZmWjfvj369u1b7H4j03N8zW7/7vLQQw/hnnvuQfv27QEAQUFBxZ5j7butEX7P6vrkUZ07d8alS5dw8eJFfPnll+qxNJ988glWr14NNzc3XLx4ERcvXrR4XkkXOT548CDGjx8PoLC7KkmSxfNq1KhR5gO1GzRoAADw8PDAsGHDsHfvXgQHByM9PV19THp6uvq4os9LS0sr9TFGp+e4AYXXBHvppZewbt06uLr+s7azYcMGLFu2DD///HOx53h7e6NTp05o1aoVAECSJCQnJ6Nt27Zo2LAhACA6OtrimqdDhw7F0KFD8c033+Dll1/Gd999V+Yx6one43k7b29vNGnSRF0tvvfee7Fv3z5ERUWpj9m0aRPOnDmDY8eO4dq1awgLC8OAAQPUL2XLli3DzJkzLV63pH83RmS0mJotXrwYBw8etDje0qyk363ffPMNYmJiUL9+/WLPSUtLw7Bhw7Bo0SI0b9680uPTit7j+dprr2Hu3LmYPn06li5dimnTpgEoPZ47duxAgwYN4OfnZ3G7t7c3TCYTPDw84OPjg7p16+Lq1asWuy4eeughPPPMM2Uen97oPZ63u379OlavXo1NmzZh69atJT6udevWiIuLw4kTJxAVFYVBgwYBsD4Piy5SjR49GmvWrMEjjzxS4TFqSc/xfPXVV/Hhhx8Wu93b21s9l8jAgQOxfv16q89/5ZVX4O3trS5IlDRvly5dihYtWmD16tVITU3F0KFD8ccff5RpjHqn5/gC1r+7TJ06FfHx8fDx8cHUqVPx7bff4sEHH7R4nrXvtnXq1FHv1+vvWV0XtgAwduxYfPTRR0hNTUX37t2RlpaGL774AklJScjNzUW7du2KncjF29tbPdHAnj171Ns7d+6MlStXolmzZgCgnvDJLCcnB/369Ss2hrCwMMyfP9/itvT0dNSvXx9CCMiyjNGjR8PDwwN16tTBiRMn0Lp1a6xfvx6ff/65xfP69OmDl156CdnZ2bh58ybOnDlTrDvoDPQat5SUFEyZMgWrV6+2WFDYunUr5s+fj/Xr11vdZuXv74/s7Gxcu3YNdevWxd69ezF27FhkZGSgZs2acHNzw4EDB9QvV1lZWerreHl5wdPTs8w/Oz3SazytcXd3R7t27ZCamgpfX1/s2rVLPQnU7eNzc3NDnTp1kJubi/z8fADApUuXkJKSYtGVLenfjZEZKaYA8O2332LVqlVYvXq11YWFkn63fvbZZ9i7dy82b96M5ORkHD58GF9//TUaN26MIUOG4LXXXkOvXr3KNAY902s8zb8LXVxcUK9ePfV34Z3iuXfvXiQlJeGee+7BsWPHUKtWLfj5+cFkMuHHH3+EEALXrl1DWloavLy8cO3aNdSrVw9A4cml2rVrV94foa7oNZ7WpKSk4Pr16xgwYACuXr2Kc+fO4bPPPrNY6M3OzlYXDuvWrYvatWsDADIyMqzOQ/N3LIDxBOwXz+PHj6tbYM+dO4fp06fjo48+QnR0NPbu3YuwsDDs2rXLYkur2VtvvYWzZ89aHJpV0rwFoG539fLyQkZGRtl+cAah1/iW9N2lWrVq6uJRo0aNLJpyAEr8bmuI37OOPqi3vK5cuSI8PT3F22+/LYQQoqCgQAwfPlwEBQWJiRMniu7du4u///7b4oDsQ4cOiW7duomBAweKGTNmqAc6Jycni379+gmTySRMJpPYsGFDhcf18MMPi6CgINGnTx+LEwMlJiaK0NBQERwcLN5880319jFjxqh//vLLL0VQUJAICgqyy0lo9HDyKL3GLTIyUrRt21ZERkaKyMhIsWbNGiGEED4+PqJLly7q7bt27RJCCPHUU0+JixcvCiGE2LJliwgLCxNBQUFi3rx5Qgghdu3aJbp16ybCw8NFeHi4SExMFEII8f7774vIyEghSZLo37+/epC+PVTleMqyLKKjo0XNmjVFdHS0+O6774QQQiQlJQlJkkRoaKiYOnWqKCgoEEL8Mw/z8vLEww8/LEJCQkRgYKB477331Nd8//33xUsvvWTxPiX9u7EHR534wkgxzc7OFtWrVxe9evVSY/DXX38JIcr3u/Xhhx9WTx41Z84c0ahRI/X1XnvttQqP+U4ccXIavcbzkUceEZGRkSIsLExMnDhR5OTklDmeZrefrPHdd98V4eHhonfv3urJqD766CMRGBgowsPDxZAhQ8SVK1cqPOY7ccTJo/Qaz5J+5xa9v+jJZczx3LZtmwgPD1d/L5vnZ0nz8OWXXxa9e/cWISEh4uGHH7Y4CZmtOPLkUXqNZ1FFTxb0999/i/79+4vIyEgxYsQI9eRu5u9EJ06cEC4uLiIsLEyN3e0xKjpvMzIyxH333SciIiJEYGCgemJGe9Di5FF6jW9J312+++479ffloEGDxI0bN4QQ/8zXkr7bOvL3bFHliamLEJW/bkViYiJ69uyJhIQE9OjRo7IvR5V0p3gwXs6F8XQujKfz+eqrr/DQQw8xpk6i6BwFwPlqUOY4rlixgvPTiTCHOp/yxNTYB4oRERERERFRlcfCloiIiIiIiAyNhS0REREREREZGgtbIiIiIiIiMjSbXu4nJSXFli9HFVTWODBezsEcR8bTOTCezufkyZMAGFNnYS2OjK3x3B4zxtA5MIc6n/LE0iZnRT516hQ6dOiAzMzMyr4U2YinpydSUlLQqlWrYvcxXs7H1dUVBQUFWg+DbITxdD5ubm7qtZLJ+Mw5FgDzqYF5enpi8+bNiImJYQydCHOo8ymtrinKJoUtUFgsXb582RYvRTbQsGHDUoPPeDmXc+fOAYB6QW8yNsbT+WRnZ8Pd3V3rYZCNFM2xzKfGZY4jY+hcmEOdz53qGjObFbZEREREREREWuDJo4iIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGgsbImIiIiIiMjQWNgSERERERGRobGwJSIiIiIiIkNjYUtERERERESGxsKWiIiIiIiIDI2FLRERERERERkaC1siIiIiIiIyNBa2REREREREZGj/BxWf2d82Buy4AAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -932,10 +932,10 @@ "id": "33fb7786", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.706833Z", - "iopub.status.busy": "2023-08-21T02:29:46.706699Z", - "iopub.status.idle": "2023-08-21T02:29:46.756824Z", - "shell.execute_reply": "2023-08-21T02:29:46.756520Z" + "iopub.execute_input": "2023-08-22T07:00:30.221897Z", + "iopub.status.busy": "2023-08-22T07:00:30.221737Z", + "iopub.status.idle": "2023-08-22T07:00:30.264092Z", + "shell.execute_reply": "2023-08-22T07:00:30.263767Z" } }, "outputs": [], @@ -967,10 +967,10 @@ "id": "a386755b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.758663Z", - "iopub.status.busy": "2023-08-21T02:29:46.758546Z", - "iopub.status.idle": "2023-08-21T02:29:46.761814Z", - "shell.execute_reply": "2023-08-21T02:29:46.761470Z" + "iopub.execute_input": "2023-08-22T07:00:30.265998Z", + "iopub.status.busy": "2023-08-22T07:00:30.265875Z", + "iopub.status.idle": "2023-08-22T07:00:30.268622Z", + "shell.execute_reply": "2023-08-22T07:00:30.268322Z" }, "lines_to_next_cell": 2 }, @@ -1013,10 +1013,10 @@ "id": "3d646928", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:46.763419Z", - "iopub.status.busy": "2023-08-21T02:29:46.763331Z", - "iopub.status.idle": "2023-08-21T02:29:47.049615Z", - "shell.execute_reply": "2023-08-21T02:29:47.049265Z" + "iopub.execute_input": "2023-08-22T07:00:30.270273Z", + "iopub.status.busy": "2023-08-22T07:00:30.270155Z", + "iopub.status.idle": "2023-08-22T07:00:30.522160Z", + "shell.execute_reply": "2023-08-22T07:00:30.521845Z" }, "lines_to_next_cell": 0 }, @@ -1074,10 +1074,10 @@ "id": "275feef7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:47.051447Z", - "iopub.status.busy": "2023-08-21T02:29:47.051323Z", - "iopub.status.idle": "2023-08-21T02:29:47.211049Z", - "shell.execute_reply": "2023-08-21T02:29:47.210740Z" + "iopub.execute_input": "2023-08-22T07:00:30.524138Z", + "iopub.status.busy": "2023-08-22T07:00:30.524014Z", + "iopub.status.idle": "2023-08-22T07:00:30.657807Z", + "shell.execute_reply": "2023-08-22T07:00:30.657403Z" }, "lines_to_next_cell": 2 }, @@ -1118,10 +1118,10 @@ "id": "01dbbef3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:47.212716Z", - "iopub.status.busy": "2023-08-21T02:29:47.212598Z", - "iopub.status.idle": "2023-08-21T02:29:47.305563Z", - "shell.execute_reply": "2023-08-21T02:29:47.305155Z" + "iopub.execute_input": "2023-08-22T07:00:30.660013Z", + "iopub.status.busy": "2023-08-22T07:00:30.659853Z", + "iopub.status.idle": "2023-08-22T07:00:30.742817Z", + "shell.execute_reply": "2023-08-22T07:00:30.742410Z" } }, "outputs": [ @@ -1171,10 +1171,10 @@ "id": "b75cc90e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:47.307949Z", - "iopub.status.busy": "2023-08-21T02:29:47.307809Z", - "iopub.status.idle": "2023-08-21T02:29:48.296516Z", - "shell.execute_reply": "2023-08-21T02:29:48.296211Z" + "iopub.execute_input": "2023-08-22T07:00:30.744837Z", + "iopub.status.busy": "2023-08-22T07:00:30.744692Z", + "iopub.status.idle": "2023-08-22T07:00:31.411169Z", + "shell.execute_reply": "2023-08-22T07:00:31.410861Z" }, "lines_to_next_cell": 0 }, @@ -1220,10 +1220,10 @@ "id": "bf9a5ed4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:48.298333Z", - "iopub.status.busy": "2023-08-21T02:29:48.298184Z", - "iopub.status.idle": "2023-08-21T02:29:48.412949Z", - "shell.execute_reply": "2023-08-21T02:29:48.412594Z" + "iopub.execute_input": "2023-08-22T07:00:31.412847Z", + "iopub.status.busy": "2023-08-22T07:00:31.412730Z", + "iopub.status.idle": "2023-08-22T07:00:31.503962Z", + "shell.execute_reply": "2023-08-22T07:00:31.503621Z" }, "lines_to_next_cell": 2 }, @@ -1263,10 +1263,10 @@ "id": "71316e9a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:48.414933Z", - "iopub.status.busy": "2023-08-21T02:29:48.414785Z", - "iopub.status.idle": "2023-08-21T02:29:48.422251Z", - "shell.execute_reply": "2023-08-21T02:29:48.421922Z" + "iopub.execute_input": "2023-08-22T07:00:31.505738Z", + "iopub.status.busy": "2023-08-22T07:00:31.505608Z", + "iopub.status.idle": "2023-08-22T07:00:31.511656Z", + "shell.execute_reply": "2023-08-22T07:00:31.511297Z" }, "lines_to_next_cell": 0 }, @@ -1421,10 +1421,10 @@ "id": "0bcc5ff1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:48.424109Z", - "iopub.status.busy": "2023-08-21T02:29:48.423996Z", - "iopub.status.idle": "2023-08-21T02:29:52.311829Z", - "shell.execute_reply": "2023-08-21T02:29:52.311492Z" + "iopub.execute_input": "2023-08-22T07:00:31.513595Z", + "iopub.status.busy": "2023-08-22T07:00:31.513453Z", + "iopub.status.idle": "2023-08-22T07:00:34.630915Z", + "shell.execute_reply": "2023-08-22T07:00:34.630577Z" } }, "outputs": [ @@ -1469,10 +1469,10 @@ "id": "060f47eb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:52.313643Z", - "iopub.status.busy": "2023-08-21T02:29:52.313522Z", - "iopub.status.idle": "2023-08-21T02:29:52.788145Z", - "shell.execute_reply": "2023-08-21T02:29:52.787823Z" + "iopub.execute_input": "2023-08-22T07:00:34.632952Z", + "iopub.status.busy": "2023-08-22T07:00:34.632784Z", + "iopub.status.idle": "2023-08-22T07:00:35.097134Z", + "shell.execute_reply": "2023-08-22T07:00:35.096800Z" } }, "outputs": [ @@ -1519,10 +1519,10 @@ "id": "43505dad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:52.789753Z", - "iopub.status.busy": "2023-08-21T02:29:52.789632Z", - "iopub.status.idle": "2023-08-21T02:29:52.801529Z", - "shell.execute_reply": "2023-08-21T02:29:52.801234Z" + "iopub.execute_input": "2023-08-22T07:00:35.100329Z", + "iopub.status.busy": "2023-08-22T07:00:35.100143Z", + "iopub.status.idle": "2023-08-22T07:00:35.112033Z", + "shell.execute_reply": "2023-08-22T07:00:35.111605Z" } }, "outputs": [ @@ -1560,10 +1560,10 @@ "id": "c0a03126", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:52.803315Z", - "iopub.status.busy": "2023-08-21T02:29:52.803174Z", - "iopub.status.idle": "2023-08-21T02:29:55.330726Z", - "shell.execute_reply": "2023-08-21T02:29:55.330425Z" + "iopub.execute_input": "2023-08-22T07:00:35.113963Z", + "iopub.status.busy": "2023-08-22T07:00:35.113830Z", + "iopub.status.idle": "2023-08-22T07:00:37.129817Z", + "shell.execute_reply": "2023-08-22T07:00:37.129514Z" }, "lines_to_next_cell": 2 }, @@ -1627,10 +1627,10 @@ "id": "58f6e11f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:55.332677Z", - "iopub.status.busy": "2023-08-21T02:29:55.332550Z", - "iopub.status.idle": "2023-08-21T02:29:56.797204Z", - "shell.execute_reply": "2023-08-21T02:29:56.796899Z" + "iopub.execute_input": "2023-08-22T07:00:37.131701Z", + "iopub.status.busy": "2023-08-22T07:00:37.131582Z", + "iopub.status.idle": "2023-08-22T07:00:38.482392Z", + "shell.execute_reply": "2023-08-22T07:00:38.481994Z" }, "lines_to_next_cell": 2 }, @@ -1668,10 +1668,10 @@ "id": "a5b1296f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:56.798892Z", - "iopub.status.busy": "2023-08-21T02:29:56.798779Z", - "iopub.status.idle": "2023-08-21T02:29:57.098494Z", - "shell.execute_reply": "2023-08-21T02:29:57.098140Z" + "iopub.execute_input": "2023-08-22T07:00:38.485515Z", + "iopub.status.busy": "2023-08-22T07:00:38.485339Z", + "iopub.status.idle": "2023-08-22T07:00:38.830191Z", + "shell.execute_reply": "2023-08-22T07:00:38.829761Z" }, "lines_to_next_cell": 2 }, @@ -1707,10 +1707,10 @@ "id": "36c52755", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:57.100444Z", - "iopub.status.busy": "2023-08-21T02:29:57.100310Z", - "iopub.status.idle": "2023-08-21T02:29:57.103577Z", - "shell.execute_reply": "2023-08-21T02:29:57.103213Z" + "iopub.execute_input": "2023-08-22T07:00:38.832666Z", + "iopub.status.busy": "2023-08-22T07:00:38.832460Z", + "iopub.status.idle": "2023-08-22T07:00:38.836173Z", + "shell.execute_reply": "2023-08-22T07:00:38.835780Z" }, "lines_to_next_cell": 0 }, @@ -1759,7 +1759,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -1772,7 +1772,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch09-svm-lab.Rmd b/Ch09-svm-lab.Rmd index 0b653ef..e08f732 100644 --- a/Ch09-svm-lab.Rmd +++ b/Ch09-svm-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch09-svm-lab.ipynb b/Ch09-svm-lab.ipynb index bf1cc7c..3b96de8 100644 --- a/Ch09-svm-lab.ipynb +++ b/Ch09-svm-lab.ipynb @@ -28,10 +28,10 @@ "id": "3973b95f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:58.477582Z", - "iopub.status.busy": "2023-08-21T02:29:58.477467Z", - "iopub.status.idle": "2023-08-21T02:29:59.432527Z", - "shell.execute_reply": "2023-08-21T02:29:59.432225Z" + "iopub.execute_input": "2023-08-22T07:00:43.196408Z", + "iopub.status.busy": "2023-08-22T07:00:43.196299Z", + "iopub.status.idle": "2023-08-22T07:00:44.031971Z", + "shell.execute_reply": "2023-08-22T07:00:44.031635Z" }, "lines_to_next_cell": 0 }, @@ -58,10 +58,10 @@ "id": "0161e55e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.434432Z", - "iopub.status.busy": "2023-08-21T02:29:59.434258Z", - "iopub.status.idle": "2023-08-21T02:29:59.466972Z", - "shell.execute_reply": "2023-08-21T02:29:59.466647Z" + "iopub.execute_input": "2023-08-22T07:00:44.034329Z", + "iopub.status.busy": "2023-08-22T07:00:44.034140Z", + "iopub.status.idle": "2023-08-22T07:00:44.066591Z", + "shell.execute_reply": "2023-08-22T07:00:44.066283Z" } }, "outputs": [], @@ -86,10 +86,10 @@ "id": "7661b056", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.469128Z", - "iopub.status.busy": "2023-08-21T02:29:59.468999Z", - "iopub.status.idle": "2023-08-21T02:29:59.470961Z", - "shell.execute_reply": "2023-08-21T02:29:59.470667Z" + "iopub.execute_input": "2023-08-22T07:00:44.068359Z", + "iopub.status.busy": "2023-08-22T07:00:44.068256Z", + "iopub.status.idle": "2023-08-22T07:00:44.070030Z", + "shell.execute_reply": "2023-08-22T07:00:44.069763Z" } }, "outputs": [], @@ -126,10 +126,10 @@ "id": "46e9ab84", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.472867Z", - "iopub.status.busy": "2023-08-21T02:29:59.472726Z", - "iopub.status.idle": "2023-08-21T02:29:59.583508Z", - "shell.execute_reply": "2023-08-21T02:29:59.583126Z" + "iopub.execute_input": "2023-08-22T07:00:44.071771Z", + "iopub.status.busy": "2023-08-22T07:00:44.071636Z", + "iopub.status.idle": "2023-08-22T07:00:44.163199Z", + "shell.execute_reply": "2023-08-22T07:00:44.162710Z" }, "lines_to_next_cell": 0 }, @@ -171,10 +171,10 @@ "id": "605ffdc0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.585485Z", - "iopub.status.busy": "2023-08-21T02:29:59.585317Z", - "iopub.status.idle": "2023-08-21T02:29:59.590274Z", - "shell.execute_reply": "2023-08-21T02:29:59.589979Z" + "iopub.execute_input": "2023-08-22T07:00:44.165309Z", + "iopub.status.busy": "2023-08-22T07:00:44.165170Z", + "iopub.status.idle": "2023-08-22T07:00:44.169891Z", + "shell.execute_reply": "2023-08-22T07:00:44.169496Z" }, "lines_to_next_cell": 2 }, @@ -215,10 +215,10 @@ "id": "302a49a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.591976Z", - "iopub.status.busy": "2023-08-21T02:29:59.591865Z", - "iopub.status.idle": "2023-08-21T02:29:59.734225Z", - "shell.execute_reply": "2023-08-21T02:29:59.733936Z" + "iopub.execute_input": "2023-08-22T07:00:44.171704Z", + "iopub.status.busy": "2023-08-22T07:00:44.171421Z", + "iopub.status.idle": "2023-08-22T07:00:44.323926Z", + "shell.execute_reply": "2023-08-22T07:00:44.323505Z" } }, "outputs": [ @@ -260,10 +260,10 @@ "id": "cc1d6a13", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.735943Z", - "iopub.status.busy": "2023-08-21T02:29:59.735816Z", - "iopub.status.idle": "2023-08-21T02:29:59.878335Z", - "shell.execute_reply": "2023-08-21T02:29:59.878032Z" + "iopub.execute_input": "2023-08-22T07:00:44.326070Z", + "iopub.status.busy": "2023-08-22T07:00:44.325931Z", + "iopub.status.idle": "2023-08-22T07:00:44.449908Z", + "shell.execute_reply": "2023-08-22T07:00:44.449566Z" }, "lines_to_next_cell": 0 }, @@ -306,10 +306,10 @@ "id": "6133c846", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.880078Z", - "iopub.status.busy": "2023-08-21T02:29:59.879965Z", - "iopub.status.idle": "2023-08-21T02:29:59.882347Z", - "shell.execute_reply": "2023-08-21T02:29:59.882070Z" + "iopub.execute_input": "2023-08-22T07:00:44.451882Z", + "iopub.status.busy": "2023-08-22T07:00:44.451759Z", + "iopub.status.idle": "2023-08-22T07:00:44.454360Z", + "shell.execute_reply": "2023-08-22T07:00:44.454092Z" }, "lines_to_next_cell": 2 }, @@ -344,10 +344,10 @@ "id": "9adb3793", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.883852Z", - "iopub.status.busy": "2023-08-21T02:29:59.883749Z", - "iopub.status.idle": "2023-08-21T02:29:59.910535Z", - "shell.execute_reply": "2023-08-21T02:29:59.910272Z" + "iopub.execute_input": "2023-08-22T07:00:44.456205Z", + "iopub.status.busy": "2023-08-22T07:00:44.456086Z", + "iopub.status.idle": "2023-08-22T07:00:44.482752Z", + "shell.execute_reply": "2023-08-22T07:00:44.482392Z" }, "lines_to_next_cell": 2 }, @@ -392,10 +392,10 @@ "id": "d3ab343e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.912005Z", - "iopub.status.busy": "2023-08-21T02:29:59.911925Z", - "iopub.status.idle": "2023-08-21T02:29:59.914189Z", - "shell.execute_reply": "2023-08-21T02:29:59.913943Z" + "iopub.execute_input": "2023-08-22T07:00:44.484837Z", + "iopub.status.busy": "2023-08-22T07:00:44.484685Z", + "iopub.status.idle": "2023-08-22T07:00:44.487229Z", + "shell.execute_reply": "2023-08-22T07:00:44.486932Z" }, "lines_to_next_cell": 0 }, @@ -433,10 +433,10 @@ "id": "6aba117e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.915563Z", - "iopub.status.busy": "2023-08-21T02:29:59.915487Z", - "iopub.status.idle": "2023-08-21T02:29:59.917323Z", - "shell.execute_reply": "2023-08-21T02:29:59.917078Z" + "iopub.execute_input": "2023-08-22T07:00:44.488910Z", + "iopub.status.busy": "2023-08-22T07:00:44.488789Z", + "iopub.status.idle": "2023-08-22T07:00:44.490857Z", + "shell.execute_reply": "2023-08-22T07:00:44.490592Z" } }, "outputs": [], @@ -462,10 +462,10 @@ "id": "dbe7d737", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.918744Z", - "iopub.status.busy": "2023-08-21T02:29:59.918666Z", - "iopub.status.idle": "2023-08-21T02:29:59.925361Z", - "shell.execute_reply": "2023-08-21T02:29:59.925039Z" + "iopub.execute_input": "2023-08-22T07:00:44.492721Z", + "iopub.status.busy": "2023-08-22T07:00:44.492565Z", + "iopub.status.idle": "2023-08-22T07:00:44.498024Z", + "shell.execute_reply": "2023-08-22T07:00:44.497699Z" } }, "outputs": [ @@ -549,10 +549,10 @@ "id": "ab1697c2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.927158Z", - "iopub.status.busy": "2023-08-21T02:29:59.927027Z", - "iopub.status.idle": "2023-08-21T02:29:59.931558Z", - "shell.execute_reply": "2023-08-21T02:29:59.931228Z" + "iopub.execute_input": "2023-08-22T07:00:44.499990Z", + "iopub.status.busy": "2023-08-22T07:00:44.499862Z", + "iopub.status.idle": "2023-08-22T07:00:44.504347Z", + "shell.execute_reply": "2023-08-22T07:00:44.504085Z" } }, "outputs": [ @@ -640,10 +640,10 @@ "id": "0fd42b1e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:59.933100Z", - "iopub.status.busy": "2023-08-21T02:29:59.933001Z", - "iopub.status.idle": "2023-08-21T02:30:00.054738Z", - "shell.execute_reply": "2023-08-21T02:30:00.054338Z" + "iopub.execute_input": "2023-08-22T07:00:44.506318Z", + "iopub.status.busy": "2023-08-22T07:00:44.506164Z", + "iopub.status.idle": "2023-08-22T07:00:44.587482Z", + "shell.execute_reply": "2023-08-22T07:00:44.587059Z" } }, "outputs": [ @@ -678,10 +678,10 @@ "id": "09c15299", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.056655Z", - "iopub.status.busy": "2023-08-21T02:30:00.056526Z", - "iopub.status.idle": "2023-08-21T02:30:00.061096Z", - "shell.execute_reply": "2023-08-21T02:30:00.060792Z" + "iopub.execute_input": "2023-08-22T07:00:44.589398Z", + "iopub.status.busy": "2023-08-22T07:00:44.589260Z", + "iopub.status.idle": "2023-08-22T07:00:44.594250Z", + "shell.execute_reply": "2023-08-22T07:00:44.593884Z" } }, "outputs": [ @@ -765,10 +765,10 @@ "id": "d5fd2ff9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.062673Z", - "iopub.status.busy": "2023-08-21T02:30:00.062585Z", - "iopub.status.idle": "2023-08-21T02:30:00.199860Z", - "shell.execute_reply": "2023-08-21T02:30:00.199129Z" + "iopub.execute_input": "2023-08-22T07:00:44.596069Z", + "iopub.status.busy": "2023-08-22T07:00:44.595957Z", + "iopub.status.idle": "2023-08-22T07:00:44.704810Z", + "shell.execute_reply": "2023-08-22T07:00:44.704515Z" }, "lines_to_next_cell": 0 }, @@ -810,10 +810,10 @@ "id": "39aff1b1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.202380Z", - "iopub.status.busy": "2023-08-21T02:30:00.202233Z", - "iopub.status.idle": "2023-08-21T02:30:00.207886Z", - "shell.execute_reply": "2023-08-21T02:30:00.207493Z" + "iopub.execute_input": "2023-08-22T07:00:44.706840Z", + "iopub.status.busy": "2023-08-22T07:00:44.706674Z", + "iopub.status.idle": "2023-08-22T07:00:44.711712Z", + "shell.execute_reply": "2023-08-22T07:00:44.711351Z" } }, "outputs": [ @@ -897,10 +897,10 @@ "id": "63a9d752", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.209907Z", - "iopub.status.busy": "2023-08-21T02:30:00.209781Z", - "iopub.status.idle": "2023-08-21T02:30:00.340803Z", - "shell.execute_reply": "2023-08-21T02:30:00.340433Z" + "iopub.execute_input": "2023-08-22T07:00:44.713664Z", + "iopub.status.busy": "2023-08-22T07:00:44.713515Z", + "iopub.status.idle": "2023-08-22T07:00:44.825679Z", + "shell.execute_reply": "2023-08-22T07:00:44.825364Z" }, "lines_to_next_cell": 2 }, @@ -950,10 +950,10 @@ "id": "2fee8df5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.342773Z", - "iopub.status.busy": "2023-08-21T02:30:00.342626Z", - "iopub.status.idle": "2023-08-21T02:30:00.345094Z", - "shell.execute_reply": "2023-08-21T02:30:00.344774Z" + "iopub.execute_input": "2023-08-22T07:00:44.827655Z", + "iopub.status.busy": "2023-08-22T07:00:44.827492Z", + "iopub.status.idle": "2023-08-22T07:00:44.829859Z", + "shell.execute_reply": "2023-08-22T07:00:44.829433Z" } }, "outputs": [], @@ -978,10 +978,10 @@ "id": "48f01abe", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.347053Z", - "iopub.status.busy": "2023-08-21T02:30:00.346902Z", - "iopub.status.idle": "2023-08-21T02:30:00.440453Z", - "shell.execute_reply": "2023-08-21T02:30:00.440153Z" + "iopub.execute_input": "2023-08-22T07:00:44.831676Z", + "iopub.status.busy": "2023-08-22T07:00:44.831542Z", + "iopub.status.idle": "2023-08-22T07:00:44.912763Z", + "shell.execute_reply": "2023-08-22T07:00:44.912396Z" }, "lines_to_next_cell": 2 }, @@ -989,7 +989,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 20, @@ -1031,10 +1031,10 @@ "id": "4acc3246", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.442257Z", - "iopub.status.busy": "2023-08-21T02:30:00.442156Z", - "iopub.status.idle": "2023-08-21T02:30:00.446674Z", - "shell.execute_reply": "2023-08-21T02:30:00.446369Z" + "iopub.execute_input": "2023-08-22T07:00:44.914642Z", + "iopub.status.busy": "2023-08-22T07:00:44.914493Z", + "iopub.status.idle": "2023-08-22T07:00:44.918862Z", + "shell.execute_reply": "2023-08-22T07:00:44.918577Z" } }, "outputs": [ @@ -1079,10 +1079,10 @@ "id": "e9852a28", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.448268Z", - "iopub.status.busy": "2023-08-21T02:30:00.448160Z", - "iopub.status.idle": "2023-08-21T02:30:00.828511Z", - "shell.execute_reply": "2023-08-21T02:30:00.828128Z" + "iopub.execute_input": "2023-08-22T07:00:44.920379Z", + "iopub.status.busy": "2023-08-22T07:00:44.920289Z", + "iopub.status.idle": "2023-08-22T07:00:45.229404Z", + "shell.execute_reply": "2023-08-22T07:00:45.229004Z" } }, "outputs": [ @@ -1123,10 +1123,10 @@ "id": "01232fc9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:00.830365Z", - "iopub.status.busy": "2023-08-21T02:30:00.830226Z", - "iopub.status.idle": "2023-08-21T02:30:01.132677Z", - "shell.execute_reply": "2023-08-21T02:30:01.132224Z" + "iopub.execute_input": "2023-08-22T07:00:45.231425Z", + "iopub.status.busy": "2023-08-22T07:00:45.231287Z", + "iopub.status.idle": "2023-08-22T07:00:45.394458Z", + "shell.execute_reply": "2023-08-22T07:00:45.394098Z" } }, "outputs": [ @@ -1167,10 +1167,10 @@ "id": "bcbd15a4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:01.134616Z", - "iopub.status.busy": "2023-08-21T02:30:01.134486Z", - "iopub.status.idle": "2023-08-21T02:30:01.243519Z", - "shell.execute_reply": "2023-08-21T02:30:01.243203Z" + "iopub.execute_input": "2023-08-22T07:00:45.396381Z", + "iopub.status.busy": "2023-08-22T07:00:45.396249Z", + "iopub.status.idle": "2023-08-22T07:00:45.487743Z", + "shell.execute_reply": "2023-08-22T07:00:45.487417Z" } }, "outputs": [ @@ -1215,10 +1215,10 @@ "id": "28ca551e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:01.245550Z", - "iopub.status.busy": "2023-08-21T02:30:01.245377Z", - "iopub.status.idle": "2023-08-21T02:30:01.600896Z", - "shell.execute_reply": "2023-08-21T02:30:01.600574Z" + "iopub.execute_input": "2023-08-22T07:00:45.489428Z", + "iopub.status.busy": "2023-08-22T07:00:45.489307Z", + "iopub.status.idle": "2023-08-22T07:00:45.761918Z", + "shell.execute_reply": "2023-08-22T07:00:45.761509Z" } }, "outputs": [ @@ -1349,10 +1349,10 @@ "id": "68ac9421", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:01.602740Z", - "iopub.status.busy": "2023-08-21T02:30:01.602614Z", - "iopub.status.idle": "2023-08-21T02:30:01.698620Z", - "shell.execute_reply": "2023-08-21T02:30:01.698322Z" + "iopub.execute_input": "2023-08-22T07:00:45.763931Z", + "iopub.status.busy": "2023-08-22T07:00:45.763759Z", + "iopub.status.idle": "2023-08-22T07:00:45.849017Z", + "shell.execute_reply": "2023-08-22T07:00:45.848298Z" }, "lines_to_next_cell": 0 }, @@ -1394,10 +1394,10 @@ "id": "f79a9e0a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:01.700479Z", - "iopub.status.busy": "2023-08-21T02:30:01.700347Z", - "iopub.status.idle": "2023-08-21T02:30:01.837479Z", - "shell.execute_reply": "2023-08-21T02:30:01.837102Z" + "iopub.execute_input": "2023-08-22T07:00:45.851590Z", + "iopub.status.busy": "2023-08-22T07:00:45.851425Z", + "iopub.status.idle": "2023-08-22T07:00:45.979876Z", + "shell.execute_reply": "2023-08-22T07:00:45.979437Z" } }, "outputs": [ @@ -1443,10 +1443,10 @@ "id": "bdb9e503", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:01.839390Z", - "iopub.status.busy": "2023-08-21T02:30:01.839243Z", - "iopub.status.idle": "2023-08-21T02:30:01.843595Z", - "shell.execute_reply": "2023-08-21T02:30:01.843287Z" + "iopub.execute_input": "2023-08-22T07:00:45.982037Z", + "iopub.status.busy": "2023-08-22T07:00:45.981884Z", + "iopub.status.idle": "2023-08-22T07:00:45.986135Z", + "shell.execute_reply": "2023-08-22T07:00:45.985717Z" } }, "outputs": [], @@ -1474,10 +1474,10 @@ "id": "329f5d2c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:01.845300Z", - "iopub.status.busy": "2023-08-21T02:30:01.845201Z", - "iopub.status.idle": "2023-08-21T02:30:01.944073Z", - "shell.execute_reply": "2023-08-21T02:30:01.943763Z" + "iopub.execute_input": "2023-08-22T07:00:45.988269Z", + "iopub.status.busy": "2023-08-22T07:00:45.988146Z", + "iopub.status.idle": "2023-08-22T07:00:46.085414Z", + "shell.execute_reply": "2023-08-22T07:00:46.084883Z" } }, "outputs": [ @@ -1529,10 +1529,10 @@ "id": "267e113d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:01.945725Z", - "iopub.status.busy": "2023-08-21T02:30:01.945611Z", - "iopub.status.idle": "2023-08-21T02:30:02.034378Z", - "shell.execute_reply": "2023-08-21T02:30:02.034069Z" + "iopub.execute_input": "2023-08-22T07:00:46.087864Z", + "iopub.status.busy": "2023-08-22T07:00:46.087700Z", + "iopub.status.idle": "2023-08-22T07:00:46.169591Z", + "shell.execute_reply": "2023-08-22T07:00:46.169145Z" } }, "outputs": [ @@ -1570,10 +1570,10 @@ "id": "64cbebd0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:02.036083Z", - "iopub.status.busy": "2023-08-21T02:30:02.035963Z", - "iopub.status.idle": "2023-08-21T02:30:03.015535Z", - "shell.execute_reply": "2023-08-21T02:30:03.014798Z" + "iopub.execute_input": "2023-08-22T07:00:46.171760Z", + "iopub.status.busy": "2023-08-22T07:00:46.171615Z", + "iopub.status.idle": "2023-08-22T07:00:46.921038Z", + "shell.execute_reply": "2023-08-22T07:00:46.920658Z" }, "lines_to_next_cell": 0 }, @@ -1634,10 +1634,10 @@ "id": "b6e6f12b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:03.017430Z", - "iopub.status.busy": "2023-08-21T02:30:03.017293Z", - "iopub.status.idle": "2023-08-21T02:30:03.099156Z", - "shell.execute_reply": "2023-08-21T02:30:03.098760Z" + "iopub.execute_input": "2023-08-22T07:00:46.923126Z", + "iopub.status.busy": "2023-08-22T07:00:46.922983Z", + "iopub.status.idle": "2023-08-22T07:00:46.997998Z", + "shell.execute_reply": "2023-08-22T07:00:46.997721Z" } }, "outputs": [ @@ -1680,10 +1680,10 @@ "id": "273a10b2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:03.101069Z", - "iopub.status.busy": "2023-08-21T02:30:03.100881Z", - "iopub.status.idle": "2023-08-21T02:30:03.130224Z", - "shell.execute_reply": "2023-08-21T02:30:03.129845Z" + "iopub.execute_input": "2023-08-22T07:00:47.000124Z", + "iopub.status.busy": "2023-08-22T07:00:46.999957Z", + "iopub.status.idle": "2023-08-22T07:00:47.027848Z", + "shell.execute_reply": "2023-08-22T07:00:47.027461Z" } }, "outputs": [ @@ -1794,10 +1794,10 @@ "id": "bc3079a7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:03.132111Z", - "iopub.status.busy": "2023-08-21T02:30:03.131975Z", - "iopub.status.idle": "2023-08-21T02:30:03.143298Z", - "shell.execute_reply": "2023-08-21T02:30:03.142948Z" + "iopub.execute_input": "2023-08-22T07:00:47.030032Z", + "iopub.status.busy": "2023-08-22T07:00:47.029880Z", + "iopub.status.idle": "2023-08-22T07:00:47.040946Z", + "shell.execute_reply": "2023-08-22T07:00:47.040538Z" } }, "outputs": [ @@ -1900,7 +1900,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -1913,7 +1913,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch10-deeplearning-lab.Rmd b/Ch10-deeplearning-lab.Rmd index 51674b6..ce234b5 100644 --- a/Ch10-deeplearning-lab.Rmd +++ b/Ch10-deeplearning-lab.Rmd @@ -1,3 +1,19 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: Rmd,ipynb + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.14.7 + kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + # Chapter 10 diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb index 2577eac..4c5a709 100644 --- a/Ch10-deeplearning-lab.ipynb +++ b/Ch10-deeplearning-lab.ipynb @@ -26,6 +26,12 @@ "execution_count": 1, "id": "cf431f3f", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:23.822772Z", + "iopub.status.busy": "2023-08-22T06:36:23.822498Z", + "iopub.status.idle": "2023-08-22T06:36:35.889421Z", + "shell.execute_reply": "2023-08-22T06:36:35.888914Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -62,7 +68,14 @@ "cell_type": "code", "execution_count": 2, "id": "1db00e03", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:35.892370Z", + "iopub.status.busy": "2023-08-22T06:36:35.892133Z", + "iopub.status.idle": "2023-08-22T06:36:37.554519Z", + "shell.execute_reply": "2023-08-22T06:36:37.554232Z" + } + }, "outputs": [], "source": [ "import torch\n", @@ -88,7 +101,14 @@ "cell_type": "code", "execution_count": 3, "id": "3da0a445", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:37.556320Z", + "iopub.status.busy": "2023-08-22T06:36:37.556190Z", + "iopub.status.idle": "2023-08-22T06:36:38.312094Z", + "shell.execute_reply": "2023-08-22T06:36:38.311799Z" + } + }, "outputs": [], "source": [ "from torchmetrics import (MeanAbsoluteError,\n", @@ -112,7 +132,14 @@ "cell_type": "code", "execution_count": 4, "id": "bbbf32fe", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.313997Z", + "iopub.status.busy": "2023-08-22T06:36:38.313882Z", + "iopub.status.idle": "2023-08-22T06:36:38.557850Z", + "shell.execute_reply": "2023-08-22T06:36:38.557519Z" + } + }, "outputs": [], "source": [ "from pytorch_lightning import Trainer\n", @@ -132,7 +159,14 @@ "cell_type": "code", "execution_count": 5, "id": "3810caf4", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.559742Z", + "iopub.status.busy": "2023-08-22T06:36:38.559626Z", + "iopub.status.idle": "2023-08-22T06:36:38.563148Z", + "shell.execute_reply": "2023-08-22T06:36:38.562883Z" + } + }, "outputs": [ { "name": "stderr", @@ -163,6 +197,12 @@ "execution_count": 6, "id": "454dc419", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.564731Z", + "iopub.status.busy": "2023-08-22T06:36:38.564619Z", + "iopub.status.idle": "2023-08-22T06:36:38.566404Z", + "shell.execute_reply": "2023-08-22T06:36:38.566156Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -198,7 +238,14 @@ "cell_type": "code", "execution_count": 7, "id": "cd43a4c6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.567754Z", + "iopub.status.busy": "2023-08-22T06:36:38.567677Z", + "iopub.status.idle": "2023-08-22T06:36:38.569838Z", + "shell.execute_reply": "2023-08-22T06:36:38.569588Z" + } + }, "outputs": [], "source": [ "from ISLP.torch import (SimpleDataModule,\n", @@ -227,7 +274,14 @@ "cell_type": "code", "execution_count": 8, "id": "eaf84e9c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.571182Z", + "iopub.status.busy": "2023-08-22T06:36:38.571106Z", + "iopub.status.idle": "2023-08-22T06:36:38.572982Z", + "shell.execute_reply": "2023-08-22T06:36:38.572746Z" + } + }, "outputs": [], "source": [ "from ISLP.torch.imdb import (load_lookup,\n", @@ -257,6 +311,12 @@ "execution_count": 9, "id": "d007a49b", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.574407Z", + "iopub.status.busy": "2023-08-22T06:36:38.574330Z", + "iopub.status.idle": "2023-08-22T06:36:38.575831Z", + "shell.execute_reply": "2023-08-22T06:36:38.575582Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -279,6 +339,12 @@ "execution_count": 10, "id": "9da64364", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.577295Z", + "iopub.status.busy": "2023-08-22T06:36:38.577214Z", + "iopub.status.idle": "2023-08-22T06:36:38.582891Z", + "shell.execute_reply": "2023-08-22T06:36:38.582646Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -307,6 +373,12 @@ "execution_count": 11, "id": "a2cfe999", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.584411Z", + "iopub.status.busy": "2023-08-22T06:36:38.584330Z", + "iopub.status.idle": "2023-08-22T06:36:38.602242Z", + "shell.execute_reply": "2023-08-22T06:36:38.601986Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -342,7 +414,14 @@ "cell_type": "code", "execution_count": 12, "id": "5c600069", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.603814Z", + "iopub.status.busy": "2023-08-22T06:36:38.603735Z", + "iopub.status.idle": "2023-08-22T06:36:38.605828Z", + "shell.execute_reply": "2023-08-22T06:36:38.605618Z" + } + }, "outputs": [], "source": [ "(X_train, \n", @@ -367,7 +446,14 @@ "cell_type": "code", "execution_count": 13, "id": "6ea4f551", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.607262Z", + "iopub.status.busy": "2023-08-22T06:36:38.607183Z", + "iopub.status.idle": "2023-08-22T06:36:38.611049Z", + "shell.execute_reply": "2023-08-22T06:36:38.610800Z" + } + }, "outputs": [ { "data": { @@ -403,7 +489,14 @@ "cell_type": "code", "execution_count": 14, "id": "f1b8b3f5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.612499Z", + "iopub.status.busy": "2023-08-22T06:36:38.612413Z", + "iopub.status.idle": "2023-08-22T06:36:38.614248Z", + "shell.execute_reply": "2023-08-22T06:36:38.614031Z" + } + }, "outputs": [], "source": [ "scaler = StandardScaler(with_mean=True, with_std=True)\n", @@ -427,6 +520,12 @@ "execution_count": 15, "id": "50ce4171", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.615637Z", + "iopub.status.busy": "2023-08-22T06:36:38.615534Z", + "iopub.status.idle": "2023-08-22T06:36:38.617783Z", + "shell.execute_reply": "2023-08-22T06:36:38.617551Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -451,7 +550,14 @@ "cell_type": "code", "execution_count": 16, "id": "94c4ab75", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:38.619363Z", + "iopub.status.busy": "2023-08-22T06:36:38.619259Z", + "iopub.status.idle": "2023-08-22T06:36:50.960206Z", + "shell.execute_reply": "2023-08-22T06:36:50.959901Z" + } + }, "outputs": [], "source": [ "cv = KFold(10,\n", @@ -479,6 +585,12 @@ "execution_count": 17, "id": "86e45999", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.962061Z", + "iopub.status.busy": "2023-08-22T06:36:50.961947Z", + "iopub.status.idle": "2023-08-22T06:36:50.964427Z", + "shell.execute_reply": "2023-08-22T06:36:50.964181Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -520,7 +632,14 @@ "cell_type": "code", "execution_count": 18, "id": "00ac7606", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.965887Z", + "iopub.status.busy": "2023-08-22T06:36:50.965780Z", + "iopub.status.idle": "2023-08-22T06:36:50.967993Z", + "shell.execute_reply": "2023-08-22T06:36:50.967736Z" + } + }, "outputs": [], "source": [ "class HittersModel(nn.Module):\n", @@ -578,7 +697,14 @@ "cell_type": "code", "execution_count": 19, "id": "bb7ff7e9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.969396Z", + "iopub.status.busy": "2023-08-22T06:36:50.969288Z", + "iopub.status.idle": "2023-08-22T06:36:50.971584Z", + "shell.execute_reply": "2023-08-22T06:36:50.971363Z" + } + }, "outputs": [], "source": [ "hit_model = HittersModel(X.shape[1])\n" @@ -614,6 +740,12 @@ "execution_count": 20, "id": "b60d34e1", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.973300Z", + "iopub.status.busy": "2023-08-22T06:36:50.973196Z", + "iopub.status.idle": "2023-08-22T06:36:50.979604Z", + "shell.execute_reply": "2023-08-22T06:36:50.979367Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -634,7 +766,7 @@ "Total params: 1,051\n", "Trainable params: 1,051\n", "Non-trainable params: 0\n", - "Total mult-adds (Units.MEGABYTES): 0.18\n", + "Total mult-adds (M): 0.18\n", "===================================================================================================================\n", "Input size (MB): 0.01\n", "Forward/backward pass size (MB): 0.07\n", @@ -682,6 +814,12 @@ "execution_count": 21, "id": "42f63682", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.981129Z", + "iopub.status.busy": "2023-08-22T06:36:50.981043Z", + "iopub.status.idle": "2023-08-22T06:36:50.983074Z", + "shell.execute_reply": "2023-08-22T06:36:50.982803Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -703,7 +841,14 @@ "cell_type": "code", "execution_count": 22, "id": "57fbf564", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.984595Z", + "iopub.status.busy": "2023-08-22T06:36:50.984509Z", + "iopub.status.idle": "2023-08-22T06:36:50.986442Z", + "shell.execute_reply": "2023-08-22T06:36:50.986202Z" + } + }, "outputs": [], "source": [ "X_test_t = torch.tensor(X_test.astype(np.float32))\n", @@ -738,7 +883,14 @@ "cell_type": "code", "execution_count": 23, "id": "570bdd73", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.987814Z", + "iopub.status.busy": "2023-08-22T06:36:50.987734Z", + "iopub.status.idle": "2023-08-22T06:36:50.989404Z", + "shell.execute_reply": "2023-08-22T06:36:50.989173Z" + } + }, "outputs": [], "source": [ "max_num_workers = rec_num_workers()" @@ -770,7 +922,14 @@ "cell_type": "code", "execution_count": 24, "id": "c08a4d6d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.990908Z", + "iopub.status.busy": "2023-08-22T06:36:50.990826Z", + "iopub.status.idle": "2023-08-22T06:36:50.992776Z", + "shell.execute_reply": "2023-08-22T06:36:50.992504Z" + } + }, "outputs": [], "source": [ "hit_dm = SimpleDataModule(hit_train,\n", @@ -798,7 +957,14 @@ "cell_type": "code", "execution_count": 25, "id": "aaa1e593", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.994186Z", + "iopub.status.busy": "2023-08-22T06:36:50.994113Z", + "iopub.status.idle": "2023-08-22T06:36:50.996729Z", + "shell.execute_reply": "2023-08-22T06:36:50.996486Z" + } + }, "outputs": [], "source": [ "hit_module = SimpleModule.regression(hit_model,\n", @@ -825,7 +991,14 @@ "cell_type": "code", "execution_count": 26, "id": "1a4e9b3c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:50.998209Z", + "iopub.status.busy": "2023-08-22T06:36:50.998126Z", + "iopub.status.idle": "2023-08-22T06:36:51.000007Z", + "shell.execute_reply": "2023-08-22T06:36:50.999781Z" + } + }, "outputs": [], "source": [ "hit_logger = CSVLogger('logs', name='hitters')" @@ -857,6 +1030,12 @@ "execution_count": 27, "id": "2f839fde", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:51.001398Z", + "iopub.status.busy": "2023-08-22T06:36:51.001321Z", + "iopub.status.idle": "2023-08-22T06:36:57.231774Z", + "shell.execute_reply": "2023-08-22T06:36:57.231473Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -883,7 +1062,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "57f1417773bf4bab9e04d2641c7de91f", "version_major": 2, "version_minor": 0 }, @@ -897,7 +1076,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f81045e13e641428a7f37ab7ceb43be", + "model_id": "4c234bd07745482e823334ca8ede4b72", "version_major": 2, "version_minor": 0 }, @@ -911,7 +1090,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8a343e9ea8e847dda642f290345ce416", "version_major": 2, "version_minor": 0 }, @@ -925,7 +1104,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "cd4b335bd692418aaa6aeaf512b165a5", "version_major": 2, "version_minor": 0 }, @@ -939,7 +1118,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "3de010d447e14f7885ad82380d0ed23d", "version_major": 2, "version_minor": 0 }, @@ -953,7 +1132,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6437dc82d36048c0aa6e29d7179b802d", "version_major": 2, "version_minor": 0 }, @@ -967,7 +1146,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "31a48327218d49048a5164d4891693c1", "version_major": 2, "version_minor": 0 }, @@ -981,7 +1160,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "f5c7646fdcdf49069bcb0dd9a71f706d", "version_major": 2, "version_minor": 0 }, @@ -995,7 +1174,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "be384b3fd24f4e57b61662c11fcf8d3a", "version_major": 2, "version_minor": 0 }, @@ -1009,7 +1188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "fc6b10ac7dd345a9845b6e8c544fbb13", "version_major": 2, "version_minor": 0 }, @@ -1023,7 +1202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2c1faeb221e74ea98b1ad4098608e47a", "version_major": 2, "version_minor": 0 }, @@ -1037,7 +1216,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "84061abe2bb84c13b533440db38ca93c", "version_major": 2, "version_minor": 0 }, @@ -1051,7 +1230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e10316b8ba324e97a57db50873e5dca7", "version_major": 2, "version_minor": 0 }, @@ -1065,7 +1244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2b7ce659cfab4b5897b5f6a37d845569", "version_major": 2, "version_minor": 0 }, @@ -1079,7 +1258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c8fc8ef9a49a4a4aba32b36fd942faa7", "version_major": 2, "version_minor": 0 }, @@ -1093,7 +1272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "1ecb329931844b47b9b767b3c80b5e32", "version_major": 2, "version_minor": 0 }, @@ -1107,7 +1286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "bdcc0adc6f664fdeb8e0b9ac5a8c351b", "version_major": 2, "version_minor": 0 }, @@ -1121,7 +1300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e780afe5ba184f3b9cb0af6732f8a464", "version_major": 2, "version_minor": 0 }, @@ -1135,7 +1314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c690985141334d6cb5927b912433934f", "version_major": 2, "version_minor": 0 }, @@ -1149,7 +1328,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "7573d869f1624ee5bedca0df253c3258", "version_major": 2, "version_minor": 0 }, @@ -1163,7 +1342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "38b1b0db2ada465797993f95bcdedf1a", "version_major": 2, "version_minor": 0 }, @@ -1177,7 +1356,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a9778179731a4b1f88c81feda8dcfcc4", "version_major": 2, "version_minor": 0 }, @@ -1191,7 +1370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a86f93dfbb57426bbc945995748d77a4", "version_major": 2, "version_minor": 0 }, @@ -1205,7 +1384,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4b66b4428ce34557934ea32daf3c1e23", "version_major": 2, "version_minor": 0 }, @@ -1219,7 +1398,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "95e913a4d43b405ab89dc36f65d1a62a", "version_major": 2, "version_minor": 0 }, @@ -1233,7 +1412,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "40b1b46633d74bbfb37ffe764573083f", "version_major": 2, "version_minor": 0 }, @@ -1247,7 +1426,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "705e9608333240d0b9e0fcb3ce46b936", "version_major": 2, "version_minor": 0 }, @@ -1261,7 +1440,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ac03f7c299544d9782de7c690c2732bb", "version_major": 2, "version_minor": 0 }, @@ -1275,7 +1454,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c27fdee8a15544c5991535a6a91f23b5", "version_major": 2, "version_minor": 0 }, @@ -1289,7 +1468,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8d04173d877f40278c2c38fecf03f347", "version_major": 2, "version_minor": 0 }, @@ -1303,7 +1482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4b44d0b98185429ca36376fd9bdd5290", "version_major": 2, "version_minor": 0 }, @@ -1317,7 +1496,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "efe93a27bde349f5a17664bb31036ccb", "version_major": 2, "version_minor": 0 }, @@ -1331,7 +1510,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6959784ed10246328e8d93ad8d4a3199", "version_major": 2, "version_minor": 0 }, @@ -1345,7 +1524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e3351d9c3ee8411b968bef75e03cd8a3", "version_major": 2, "version_minor": 0 }, @@ -1359,7 +1538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "bce44e02e5df444fbffb7d9584c91654", "version_major": 2, "version_minor": 0 }, @@ -1373,7 +1552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b438f2d5ab2e4dcd807c83bf9f9590e3", "version_major": 2, "version_minor": 0 }, @@ -1387,7 +1566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e3861a097df54b019bbb8679c5998d55", "version_major": 2, "version_minor": 0 }, @@ -1401,7 +1580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "90b4d7b3e5f54a33bd8f45b1db5ac9a5", "version_major": 2, "version_minor": 0 }, @@ -1415,7 +1594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "d8934eb8b16f4a0ca89d18a2ea942c2b", "version_major": 2, "version_minor": 0 }, @@ -1429,7 +1608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8fa19f79b5b24960a0406b57aec15d97", "version_major": 2, "version_minor": 0 }, @@ -1443,7 +1622,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "47fc7a6d129c4fbe8ffeec0ae5462473", "version_major": 2, "version_minor": 0 }, @@ -1457,7 +1636,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "7db3b75a263f4ddda899eb29f5cfaef3", "version_major": 2, "version_minor": 0 }, @@ -1471,7 +1650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "26e3ef4daa1040c08318f410c9ce7301", "version_major": 2, "version_minor": 0 }, @@ -1485,7 +1664,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2757386cc60944f8882392a13593aea3", "version_major": 2, "version_minor": 0 }, @@ -1499,7 +1678,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4b978e6606314196ab8e5357263bdf46", "version_major": 2, "version_minor": 0 }, @@ -1513,7 +1692,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "35250da3c5d24ac6be004b8377f0471f", "version_major": 2, "version_minor": 0 }, @@ -1527,7 +1706,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "595bb83f8eda4b4bad9cc570604cfeda", "version_major": 2, "version_minor": 0 }, @@ -1541,7 +1720,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "1533565239a74701823302f09bf1bc44", "version_major": 2, "version_minor": 0 }, @@ -1555,7 +1734,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "336786699ff5436da313a46db847fab4", "version_major": 2, "version_minor": 0 }, @@ -1569,7 +1748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "553e586e7cd54ad3bb9e01d0fc37754e", + "model_id": "cf95bde69fa14a1eb0a348a4c394f678", "version_major": 2, "version_minor": 0 }, @@ -1583,7 +1762,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05c22b9bdd4c48098756a37b57fc963b", + "model_id": "7aca029ff1bd4a159fa095fe2a761a79", "version_major": 2, "version_minor": 0 }, @@ -1597,7 +1776,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f073bf03d90b4e318352c5de82bb9953", + "model_id": "99ec7e76a90e4d32aa129b8a5432f9b1", "version_major": 2, "version_minor": 0 }, @@ -1645,13 +1824,19 @@ "execution_count": 28, "id": "672b4410", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:57.233763Z", + "iopub.status.busy": "2023-08-22T06:36:57.233589Z", + "iopub.status.idle": "2023-08-22T06:36:58.499876Z", + "shell.execute_reply": "2023-08-22T06:36:58.499595Z" + }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.501449Z", + "iopub.status.busy": "2023-08-22T06:36:58.501342Z", + "iopub.status.idle": "2023-08-22T06:36:58.505142Z", + "shell.execute_reply": "2023-08-22T06:36:58.504888Z" + } + }, "outputs": [], "source": [ "hit_results = pd.read_csv(hit_logger.experiment.metrics_file_path)" @@ -1741,6 +1920,12 @@ "execution_count": 30, "id": "67ce1e26", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.506556Z", + "iopub.status.busy": "2023-08-22T06:36:58.506485Z", + "iopub.status.idle": "2023-08-22T06:36:58.508767Z", + "shell.execute_reply": "2023-08-22T06:36:58.508514Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1784,6 +1969,12 @@ "execution_count": 31, "id": "deb684d2", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.510129Z", + "iopub.status.busy": "2023-08-22T06:36:58.510060Z", + "iopub.status.idle": "2023-08-22T06:36:58.709656Z", + "shell.execute_reply": "2023-08-22T06:36:58.700996Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -1831,6 +2022,12 @@ "execution_count": 32, "id": "454033dd", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.732799Z", + "iopub.status.busy": "2023-08-22T06:36:58.729950Z", + "iopub.status.idle": "2023-08-22T06:36:58.780581Z", + "shell.execute_reply": "2023-08-22T06:36:58.765673Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -1877,6 +2074,12 @@ "execution_count": 33, "id": "71b3d0d0", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.807133Z", + "iopub.status.busy": "2023-08-22T06:36:58.800859Z", + "iopub.status.idle": "2023-08-22T06:36:58.836318Z", + "shell.execute_reply": "2023-08-22T06:36:58.822052Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -1909,7 +2112,14 @@ "cell_type": "code", "execution_count": 34, "id": "def8605c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.852033Z", + "iopub.status.busy": "2023-08-22T06:36:58.848358Z", + "iopub.status.idle": "2023-08-22T06:36:58.873333Z", + "shell.execute_reply": "2023-08-22T06:36:58.873077Z" + } + }, "outputs": [ { "data": { @@ -1964,7 +2174,14 @@ "cell_type": "code", "execution_count": 35, "id": "8b9e2b8c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.874850Z", + "iopub.status.busy": "2023-08-22T06:36:58.874776Z", + "iopub.status.idle": "2023-08-22T06:36:58.877600Z", + "shell.execute_reply": "2023-08-22T06:36:58.877374Z" + } + }, "outputs": [], "source": [ "mnist_dm = SimpleDataModule(mnist_train,\n", @@ -1988,6 +2205,12 @@ "execution_count": 36, "id": "a4b95dc6", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:36:58.879121Z", + "iopub.status.busy": "2023-08-22T06:36:58.879027Z", + "iopub.status.idle": "2023-08-22T06:37:01.493098Z", + "shell.execute_reply": "2023-08-22T06:37:01.492695Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -2026,7 +2249,14 @@ "cell_type": "code", "execution_count": 37, "id": "17714c25", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:37:01.495059Z", + "iopub.status.busy": "2023-08-22T06:37:01.494927Z", + "iopub.status.idle": "2023-08-22T06:37:01.497746Z", + "shell.execute_reply": "2023-08-22T06:37:01.497412Z" + } + }, "outputs": [], "source": [ "class MNISTModel(nn.Module):\n", @@ -2066,7 +2296,14 @@ "cell_type": "code", "execution_count": 38, "id": "88a4bf46", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:37:01.499269Z", + "iopub.status.busy": "2023-08-22T06:37:01.499164Z", + "iopub.status.idle": "2023-08-22T06:37:01.501783Z", + "shell.execute_reply": "2023-08-22T06:37:01.501554Z" + } + }, "outputs": [], "source": [ "mnist_model = MNISTModel()\n" @@ -2085,7 +2322,14 @@ "cell_type": "code", "execution_count": 39, "id": "ea0d9387", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:37:01.503288Z", + "iopub.status.busy": "2023-08-22T06:37:01.503188Z", + "iopub.status.idle": "2023-08-22T06:37:01.506547Z", + "shell.execute_reply": "2023-08-22T06:37:01.506292Z" + } + }, "outputs": [ { "data": { @@ -2116,7 +2360,14 @@ "cell_type": "code", "execution_count": 40, "id": "17c34a29", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:37:01.507963Z", + "iopub.status.busy": "2023-08-22T06:37:01.507888Z", + "iopub.status.idle": "2023-08-22T06:37:01.511369Z", + "shell.execute_reply": "2023-08-22T06:37:01.511148Z" + } + }, "outputs": [ { "data": { @@ -2140,7 +2391,7 @@ "Total params: 235,146\n", "Trainable params: 235,146\n", "Non-trainable params: 0\n", - "Total mult-adds (Units.MEGABYTES): 60.20\n", + "Total mult-adds (M): 60.20\n", "===================================================================================================================\n", "Input size (MB): 0.80\n", "Forward/backward pass size (MB): 0.81\n", @@ -2178,7 +2429,14 @@ "cell_type": "code", "execution_count": 41, "id": "a0608bd1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:37:01.512793Z", + "iopub.status.busy": "2023-08-22T06:37:01.512716Z", + "iopub.status.idle": "2023-08-22T06:37:01.515484Z", + "shell.execute_reply": "2023-08-22T06:37:01.515264Z" + } + }, "outputs": [], "source": [ "mnist_module = SimpleModule.classification(mnist_model,\n", @@ -2199,6 +2457,12 @@ "execution_count": 42, "id": "cf8e3d9d", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:37:01.516943Z", + "iopub.status.busy": "2023-08-22T06:37:01.516871Z", + "iopub.status.idle": "2023-08-22T06:37:52.281809Z", + "shell.execute_reply": "2023-08-22T06:37:52.281494Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -2225,7 +2489,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "63df57862e174cfbac1b87dcdce84e5b", "version_major": 2, "version_minor": 0 }, @@ -2239,7 +2503,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c7fc7b3fc61455b88cf7020ce62d19e", + "model_id": "0c8fa4bf11bc406ca04772133304a80b", "version_major": 2, "version_minor": 0 }, @@ -2253,7 +2517,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "286d40af4ac7465fa859e111186e92a2", "version_major": 2, "version_minor": 0 }, @@ -2267,7 +2531,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b34683237d33463fb3e956721f9e93a0", "version_major": 2, "version_minor": 0 }, @@ -2281,7 +2545,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8f17ac7cff5a42238da4984e05d2d712", "version_major": 2, "version_minor": 0 }, @@ -2295,7 +2559,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "856884c4ba3140fbbb44c8d3860be17d", "version_major": 2, "version_minor": 0 }, @@ -2309,7 +2573,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9ed1badd6c7f42458216610a2bd88f1d", "version_major": 2, "version_minor": 0 }, @@ -2323,7 +2587,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a27211d050d742dda389aaaa17816a49", "version_major": 2, "version_minor": 0 }, @@ -2337,7 +2601,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "dced6bafd5f243d981b97c504f026b7f", "version_major": 2, "version_minor": 0 }, @@ -2351,7 +2615,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "86b5e3b7319948c58d1a0f287a6a4270", "version_major": 2, "version_minor": 0 }, @@ -2365,7 +2629,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ab0715c2a7f1471880888a605ab86282", "version_major": 2, "version_minor": 0 }, @@ -2379,7 +2643,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9464a6e45dfb43738c7874084ac2511e", "version_major": 2, "version_minor": 0 }, @@ -2393,7 +2657,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a5651d6107914f21bc97a9fd2c20b000", "version_major": 2, "version_minor": 0 }, @@ -2407,7 +2671,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b37a8d5ca7304c59a1664ab01623ea38", "version_major": 2, "version_minor": 0 }, @@ -2421,7 +2685,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "52f72ebf2fa746678a69ebd20a2ae421", "version_major": 2, "version_minor": 0 }, @@ -2435,7 +2699,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "574b08674760459b81784861b17f6075", "version_major": 2, "version_minor": 0 }, @@ -2449,7 +2713,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9b79941ecf2d47629bb468627f87304d", "version_major": 2, "version_minor": 0 }, @@ -2463,7 +2727,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "afd5fd2289b14774abfecb664ae8f1f6", "version_major": 2, "version_minor": 0 }, @@ -2477,7 +2741,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1_Ch10/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", " warning_cache.warn(\n", "\n", " | Name | Type | Params\n", @@ -2885,7 +3162,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "bfb904663981447bb712602e62891de1", "version_major": 2, "version_minor": 0 }, @@ -2899,7 +3176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c932821add0e4296a5ff2122a94b6090", + "model_id": "e3364eae3d824a26aaf19ba7738f28b9", "version_major": 2, "version_minor": 0 }, @@ -2913,7 +3190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e1dc3c094ca54b96ae09ea1c3f81cc96", "version_major": 2, "version_minor": 0 }, @@ -2927,7 +3204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4a3e74d53cf140a29a4d1b01260bf530", "version_major": 2, "version_minor": 0 }, @@ -2941,7 +3218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:33.746175Z", + "iopub.status.busy": "2023-08-22T06:38:33.746084Z", + "iopub.status.idle": "2023-08-22T06:38:35.260793Z", + "shell.execute_reply": "2023-08-22T06:38:35.260496Z" + } + }, "outputs": [ { "name": "stdout", @@ -3485,7 +3768,14 @@ "cell_type": "code", "execution_count": 50, "id": "2b613ecc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:35.262479Z", + "iopub.status.busy": "2023-08-22T06:38:35.262391Z", + "iopub.status.idle": "2023-08-22T06:38:36.005488Z", + "shell.execute_reply": "2023-08-22T06:38:36.005166Z" + } + }, "outputs": [], "source": [ "transform = ToTensor()\n", @@ -3517,6 +3807,12 @@ "execution_count": 51, "id": "4b325cb4", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:36.007353Z", + "iopub.status.busy": "2023-08-22T06:38:36.007234Z", + "iopub.status.idle": "2023-08-22T06:38:36.010064Z", + "shell.execute_reply": "2023-08-22T06:38:36.009819Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -3541,6 +3837,12 @@ "execution_count": 52, "id": "cb3d00cb", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:36.011610Z", + "iopub.status.busy": "2023-08-22T06:38:36.011502Z", + "iopub.status.idle": "2023-08-22T06:38:38.616192Z", + "shell.execute_reply": "2023-08-22T06:38:38.615836Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -3580,6 +3882,12 @@ "execution_count": 53, "id": "60d09656", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:38.618165Z", + "iopub.status.busy": "2023-08-22T06:38:38.618040Z", + "iopub.status.idle": "2023-08-22T06:38:38.959064Z", + "shell.execute_reply": "2023-08-22T06:38:38.950625Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -3629,7 +3937,14 @@ "cell_type": "code", "execution_count": 54, "id": "f823da11", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:38.970097Z", + "iopub.status.busy": "2023-08-22T06:38:38.969929Z", + "iopub.status.idle": "2023-08-22T06:38:38.990599Z", + "shell.execute_reply": "2023-08-22T06:38:38.982099Z" + } + }, "outputs": [], "source": [ "class BuildingBlock(nn.Module):\n", @@ -3674,7 +3989,14 @@ "cell_type": "code", "execution_count": 55, "id": "1a172f7e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:39.009337Z", + "iopub.status.busy": "2023-08-22T06:38:39.007564Z", + "iopub.status.idle": "2023-08-22T06:38:39.027768Z", + "shell.execute_reply": "2023-08-22T06:38:39.017688Z" + } + }, "outputs": [], "source": [ "class CIFARModel(nn.Module):\n", @@ -3711,6 +4033,12 @@ "execution_count": 56, "id": "651e62b4", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:39.041161Z", + "iopub.status.busy": "2023-08-22T06:38:39.040974Z", + "iopub.status.idle": "2023-08-22T06:38:39.147687Z", + "shell.execute_reply": "2023-08-22T06:38:39.147402Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -3747,7 +4075,7 @@ "Total params: 964,516\n", "Trainable params: 964,516\n", "Non-trainable params: 0\n", - "Total mult-adds (Units.GIGABYTES): 2.01\n", + "Total mult-adds (G): 2.01\n", "===================================================================================================================\n", "Input size (MB): 1.57\n", "Forward/backward pass size (MB): 63.54\n", @@ -3807,7 +4135,14 @@ "cell_type": "code", "execution_count": 57, "id": "63f2650e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:39.149388Z", + "iopub.status.busy": "2023-08-22T06:38:39.149267Z", + "iopub.status.idle": "2023-08-22T06:38:39.152471Z", + "shell.execute_reply": "2023-08-22T06:38:39.152156Z" + } + }, "outputs": [], "source": [ "cifar_optimizer = RMSprop(cifar_model.parameters(), lr=0.001)\n", @@ -3821,7 +4156,14 @@ "cell_type": "code", "execution_count": 58, "id": "a3e4bc28", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:38:39.153978Z", + "iopub.status.busy": "2023-08-22T06:38:39.153895Z", + "iopub.status.idle": "2023-08-22T06:41:13.243974Z", + "shell.execute_reply": "2023-08-22T06:41:13.243600Z" + } + }, "outputs": [ { "name": "stderr", @@ -3846,7 +4188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "cf75ac2267f9488babf1c88067185953", "version_major": 2, "version_minor": 0 }, @@ -3860,7 +4202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40c811fa26da4690a95838e6ab0a8a98", + "model_id": "30da5d15249f473bb2e55004a4fbc6fc", "version_major": 2, "version_minor": 0 }, @@ -3874,7 +4216,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "75f26bea217748b1b0bfb0b97b06be25", "version_major": 2, "version_minor": 0 }, @@ -3888,7 +4230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "abca09d01792423dbe50d4e71ea8bb2b", "version_major": 2, "version_minor": 0 }, @@ -3902,7 +4244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "063695dad311441ebcfa6408148a54f2", "version_major": 2, "version_minor": 0 }, @@ -3916,7 +4258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e0b1014a1bf24b4aa0a0062a215ecf06", "version_major": 2, "version_minor": 0 }, @@ -3930,7 +4272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c06e825534814c1f8942b2fd0e719833", "version_major": 2, "version_minor": 0 }, @@ -3944,7 +4286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4d3a2c2b34ce4cdba5387b4066c33bc1", "version_major": 2, "version_minor": 0 }, @@ -3958,7 +4300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ca1c1a95c02f46588c9603928ad2c8eb", "version_major": 2, "version_minor": 0 }, @@ -3972,7 +4314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "242fa898b94a40579e6a9331a5039240", "version_major": 2, "version_minor": 0 }, @@ -3986,7 +4328,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "7ad231ef819943fea0f68783c6e0ed4d", "version_major": 2, "version_minor": 0 }, @@ -4000,7 +4342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9b786b6275fd4693b006ef6e529e3227", "version_major": 2, "version_minor": 0 }, @@ -4014,7 +4356,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "5e42a316520f44ada04f9ce2978daa8a", "version_major": 2, "version_minor": 0 }, @@ -4028,7 +4370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e9e3bd085ffd4dd68056e252d761edde", "version_major": 2, "version_minor": 0 }, @@ -4042,7 +4384,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4c7702499c474ebe887d17f110b136f7", "version_major": 2, "version_minor": 0 }, @@ -4056,7 +4398,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "3e38f9ac5d644c8b914fcda42d71fd30", "version_major": 2, "version_minor": 0 }, @@ -4070,7 +4412,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "12159193294842879e2abf2a6a57542c", "version_major": 2, "version_minor": 0 }, @@ -4084,7 +4426,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "d71292277b484c9ebcf31c730a7aece1", "version_major": 2, "version_minor": 0 }, @@ -4098,7 +4440,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "0016c30eadbe49ababf5b92c7fcf2f2b", "version_major": 2, "version_minor": 0 }, @@ -4112,7 +4454,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "feef8645a1134646b9ecde5310876ae1", "version_major": 2, "version_minor": 0 }, @@ -4126,7 +4468,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e30a991ac871447e98466cea30cc1e9b", "version_major": 2, "version_minor": 0 }, @@ -4140,7 +4482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2ecaf65a04ee4b0698d666dcfdad463a", "version_major": 2, "version_minor": 0 }, @@ -4154,7 +4496,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8ca015472e6e49d5bf04f6eb6c147da4", "version_major": 2, "version_minor": 0 }, @@ -4168,7 +4510,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "7c265e3e36c94d0196236e5ef3aa587f", "version_major": 2, "version_minor": 0 }, @@ -4182,7 +4524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "161a3af371ca4d60a28dcafe05d27e64", "version_major": 2, "version_minor": 0 }, @@ -4196,7 +4538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e928c858bece4168a70610230c811c85", "version_major": 2, "version_minor": 0 }, @@ -4210,7 +4552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2f8a344b80b14c0bbeca2fbafc3c7b8c", "version_major": 2, "version_minor": 0 }, @@ -4224,7 +4566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "0a46cdb4c1b3476f9a6805a44088fed3", "version_major": 2, "version_minor": 0 }, @@ -4238,7 +4580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "150df772fdeb468c8ae3dd482aba37d4", "version_major": 2, "version_minor": 0 }, @@ -4252,7 +4594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "0d23b1967cfb439eaf23fd1c7d80aa58", "version_major": 2, "version_minor": 0 }, @@ -4266,7 +4608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "", + "model_id": "8561f251621b4b4bb80996645ee21113", "version_major": 2, "version_minor": 0 }, @@ -4491,7 +4838,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6409051044c94ad9af785a42df78d36e", + "model_id": "6ec66d48c6cb4c3d946961e005a8a479", "version_major": 2, "version_minor": 0 }, @@ -4506,14 +4853,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/torchmetrics/functional/classification/accuracy.py:77: UserWarning: MPS: no support for int64 reduction ops, casting it to int32 (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/ReduceOps.mm:144.)\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1_Ch10/lib/python3.10/site-packages/torchmetrics/functional/classification/accuracy.py:77: UserWarning: MPS: no support for int64 reduction ops, casting it to int32 (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/ReduceOps.mm:144.)\n", " tp = tp.sum(dim=0 if multidim_average == \"global\" else 1)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4d4700f8a59d48909fab4b18101147f7", "version_major": 2, "version_minor": 0 }, @@ -4527,7 +4874,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "3818f316eaca4ba495994364785ace29", "version_major": 2, "version_minor": 0 }, @@ -4541,7 +4888,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "733bcce394364359a4f1377471a556f3", "version_major": 2, "version_minor": 0 }, @@ -4555,7 +4902,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "741fbb3b9bb34571acfbe63d2cfc83b1", "version_major": 2, "version_minor": 0 }, @@ -4569,7 +4916,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "058f545200134cddb98ab49116a4b5b6", "version_major": 2, "version_minor": 0 }, @@ -4583,7 +4930,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2ef3ac0704564c54a5f010a467cb17b8", "version_major": 2, "version_minor": 0 }, @@ -4597,7 +4944,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6813b7f3eeab473ea3232bcb1a8b805e", "version_major": 2, "version_minor": 0 }, @@ -4611,7 +4958,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8fd851ce512b410b8a7c90b1ca2d4d88", "version_major": 2, "version_minor": 0 }, @@ -4625,7 +4972,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b07cf4aa47a84e4186a0184c774f1b62", "version_major": 2, "version_minor": 0 }, @@ -4639,7 +4986,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "74c3c3d036394b729679bb50be07979f", "version_major": 2, "version_minor": 0 }, @@ -4653,7 +5000,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4d9b17ce0c264604ad78cca0b4a95c83", "version_major": 2, "version_minor": 0 }, @@ -4667,7 +5014,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c169f02b4204490bb077558716d3774e", "version_major": 2, "version_minor": 0 }, @@ -4681,7 +5028,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "18a31aba978d4a8692111fb6c55fe85d", "version_major": 2, "version_minor": 0 }, @@ -4695,7 +5042,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c520605694ca49158bca1679e5987432", "version_major": 2, "version_minor": 0 }, @@ -4709,7 +5056,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "59894643e27a46a091c3c42ffd1e9797", "version_major": 2, "version_minor": 0 }, @@ -4723,7 +5070,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "5adba0c8771a46e9a4b64c5492134377", "version_major": 2, "version_minor": 0 }, @@ -4737,7 +5084,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "05cc7d17519247d4bed8d0260fd8c9c6", "version_major": 2, "version_minor": 0 }, @@ -4751,7 +5098,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "48808854ac234c8ca63b5e5b273f17bd", "version_major": 2, "version_minor": 0 }, @@ -4765,7 +5112,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "92cf213e529d4e34be6d8473a8ca6b23", "version_major": 2, "version_minor": 0 }, @@ -4779,7 +5126,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ec82fcb270004491a95123102e13a852", "version_major": 2, "version_minor": 0 }, @@ -4793,7 +5140,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "5abf5e8af169412a9ceef746437ccaf4", "version_major": 2, "version_minor": 0 }, @@ -4807,7 +5154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "978f680372c846dabc500b9ccb9b80d8", "version_major": 2, "version_minor": 0 }, @@ -4821,7 +5168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ac9a9c30854f4607a35ddb6623a312b6", "version_major": 2, "version_minor": 0 }, @@ -4835,7 +5182,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ad5df9b2fa754520af1210fe3e8e8406", "version_major": 2, "version_minor": 0 }, @@ -4849,7 +5196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "27ff88a097934fecb6d5897713435267", "version_major": 2, "version_minor": 0 }, @@ -4863,7 +5210,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "243e06349f03454cbcab5ecd2d67f896", "version_major": 2, "version_minor": 0 }, @@ -4877,7 +5224,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9443d9fd5d7b45649a37e5cc1f52c58d", "version_major": 2, "version_minor": 0 }, @@ -4891,7 +5238,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c531fb2f7d5244ad8d0c0e4a4ee028c7", "version_major": 2, "version_minor": 0 }, @@ -4905,7 +5252,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b5460433203542fea838b387894252c0", "version_major": 2, "version_minor": 0 }, @@ -4919,7 +5266,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c7c5e5f98571429b8dd82815442cd626", "version_major": 2, "version_minor": 0 }, @@ -4940,7 +5287,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f37d3b614314b6fbf9dfb3df1775948", + "model_id": "486106ba7b134b3682fc506c03ebce5e", "version_major": 2, "version_minor": 0 }, @@ -5003,6 +5350,12 @@ "execution_count": 62, "id": "a71c9acb", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:01.469991Z", + "iopub.status.busy": "2023-08-22T06:44:01.469875Z", + "iopub.status.idle": "2023-08-22T06:44:01.935872Z", + "shell.execute_reply": "2023-08-22T06:44:01.935582Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -5042,6 +5395,12 @@ "execution_count": 63, "id": "4f890244", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:01.937487Z", + "iopub.status.busy": "2023-08-22T06:44:01.937384Z", + "iopub.status.idle": "2023-08-22T06:44:02.437287Z", + "shell.execute_reply": "2023-08-22T06:44:02.436974Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -5230,7 +5589,7 @@ "Total params: 25,557,032\n", "Trainable params: 25,557,032\n", "Non-trainable params: 0\n", - "Total mult-adds (Units.GIGABYTES): 24.54\n", + "Total mult-adds (G): 24.54\n", "===================================================================================================================\n", "Input size (MB): 3.61\n", "Forward/backward pass size (MB): 1066.99\n", @@ -5266,6 +5625,12 @@ "execution_count": 64, "id": "c4be9922", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.438850Z", + "iopub.status.busy": "2023-08-22T06:44:02.438741Z", + "iopub.status.idle": "2023-08-22T06:44:02.441925Z", + "shell.execute_reply": "2023-08-22T06:44:02.441612Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -5475,7 +5840,14 @@ "cell_type": "code", "execution_count": 65, "id": "2dc63d85", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.443425Z", + "iopub.status.busy": "2023-08-22T06:44:02.443344Z", + "iopub.status.idle": "2023-08-22T06:44:02.567717Z", + "shell.execute_reply": "2023-08-22T06:44:02.567412Z" + } + }, "outputs": [], "source": [ "img_preds = resnet_model(imgs)\n" @@ -5496,7 +5868,14 @@ "cell_type": "code", "execution_count": 66, "id": "711d5ba7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.569455Z", + "iopub.status.busy": "2023-08-22T06:44:02.569371Z", + "iopub.status.idle": "2023-08-22T06:44:02.571608Z", + "shell.execute_reply": "2023-08-22T06:44:02.571356Z" + } + }, "outputs": [], "source": [ "img_probs = np.exp(np.asarray(img_preds.detach()))\n", @@ -5515,7 +5894,14 @@ "cell_type": "code", "execution_count": 67, "id": "b22f70d8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.573175Z", + "iopub.status.busy": "2023-08-22T06:44:02.573082Z", + "iopub.status.idle": "2023-08-22T06:44:02.580482Z", + "shell.execute_reply": "2023-08-22T06:44:02.580237Z" + } + }, "outputs": [], "source": [ "labs = json.load(open('imagenet_class_index.json'))\n", @@ -5541,6 +5927,12 @@ "execution_count": 68, "id": "b19c6bd1", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.581982Z", + "iopub.status.busy": "2023-08-22T06:44:02.581889Z", + "iopub.status.idle": "2023-08-22T06:44:02.593476Z", + "shell.execute_reply": "2023-08-22T06:44:02.593210Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -5607,6 +5999,12 @@ "execution_count": 69, "id": "ba80b615", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.594912Z", + "iopub.status.busy": "2023-08-22T06:44:02.594816Z", + "iopub.status.idle": "2023-08-22T06:44:02.596527Z", + "shell.execute_reply": "2023-08-22T06:44:02.596344Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -5651,6 +6049,12 @@ "execution_count": 70, "id": "ba6d2d2c", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.597905Z", + "iopub.status.busy": "2023-08-22T06:44:02.597817Z", + "iopub.status.idle": "2023-08-22T06:44:02.626372Z", + "shell.execute_reply": "2023-08-22T06:44:02.626104Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -5695,7 +6099,14 @@ "cell_type": "code", "execution_count": 71, "id": "93bda908", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.627866Z", + "iopub.status.busy": "2023-08-22T06:44:02.627766Z", + "iopub.status.idle": "2023-08-22T06:44:02.638183Z", + "shell.execute_reply": "2023-08-22T06:44:02.637892Z" + } + }, "outputs": [ { "data": { @@ -5730,6 +6141,12 @@ "execution_count": 72, "id": "40943b7d", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:02.639670Z", + "iopub.status.busy": "2023-08-22T06:44:02.639577Z", + "iopub.status.idle": "2023-08-22T06:44:03.265220Z", + "shell.execute_reply": "2023-08-22T06:44:03.264917Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -5757,6 +6174,12 @@ "execution_count": 73, "id": "2117fd9f", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:03.267054Z", + "iopub.status.busy": "2023-08-22T06:44:03.266963Z", + "iopub.status.idle": "2023-08-22T06:44:03.269380Z", + "shell.execute_reply": "2023-08-22T06:44:03.269155Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -5793,7 +6216,14 @@ "cell_type": "code", "execution_count": 74, "id": "66d0b710", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:03.270876Z", + "iopub.status.busy": "2023-08-22T06:44:03.270774Z", + "iopub.status.idle": "2023-08-22T06:44:04.097412Z", + "shell.execute_reply": "2023-08-22T06:44:04.097126Z" + } + }, "outputs": [ { "data": { @@ -5811,7 +6241,7 @@ "Total params: 160,353\n", "Trainable params: 160,353\n", "Non-trainable params: 0\n", - "Total mult-adds (Units.GIGABYTES): 4.01\n", + "Total mult-adds (G): 4.01\n", "===================================================================================================================\n", "Input size (MB): 1000.30\n", "Forward/backward pass size (MB): 6.60\n", @@ -5855,7 +6285,14 @@ "cell_type": "code", "execution_count": 75, "id": "9df8b4cf", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:04.101984Z", + "iopub.status.busy": "2023-08-22T06:44:04.101529Z", + "iopub.status.idle": "2023-08-22T06:44:04.111524Z", + "shell.execute_reply": "2023-08-22T06:44:04.110929Z" + } + }, "outputs": [], "source": [ "imdb_optimizer = RMSprop(imdb_model.parameters(), lr=0.001)\n", @@ -5878,7 +6315,14 @@ "cell_type": "code", "execution_count": 76, "id": "73684c66", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:04.113582Z", + "iopub.status.busy": "2023-08-22T06:44:04.113437Z", + "iopub.status.idle": "2023-08-22T06:44:22.590794Z", + "shell.execute_reply": "2023-08-22T06:44:22.590448Z" + } + }, "outputs": [ { "name": "stderr", @@ -5903,7 +6347,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "795333e5d7b84fc6812fcf3dd6391bfa", "version_major": 2, "version_minor": 0 }, @@ -5918,14 +6362,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/pytorch_lightning/loops/fit_loop.py:280: PossibleUserWarning: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", + "/Users/jonathantaylor/anaconda3/envs/isolated_env_1eb44da1_Ch10/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:280: PossibleUserWarning: The number of training batches (45) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", " rank_zero_warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "253cf3e077d845569cdc459ef74902b6", + "model_id": "7c1ce82c29a644f68f5706783e08927a", "version_major": 2, "version_minor": 0 }, @@ -5939,7 +6383,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b1ac59bc49094a708bfa29f4fc194a71", "version_major": 2, "version_minor": 0 }, @@ -5953,7 +6397,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "21a870683e2a4e0ebb2b86c8c9afeb53", "version_major": 2, "version_minor": 0 }, @@ -5967,7 +6411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8bae27914733459abdb5e8df79747137", "version_major": 2, "version_minor": 0 }, @@ -5981,7 +6425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8804e3a20d1c49958518a664bd63f369", "version_major": 2, "version_minor": 0 }, @@ -5995,7 +6439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "06686ccf9eea46a498c78fc6ed739fc2", "version_major": 2, "version_minor": 0 }, @@ -6009,7 +6453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "4054f655d9a048e380f86adadf5b1312", "version_major": 2, "version_minor": 0 }, @@ -6023,7 +6467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "f5f469b4c14a4a6687acd450b5c0ad69", "version_major": 2, "version_minor": 0 }, @@ -6037,7 +6481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "feccfd4b4d3b4bc8835c5e0e332f18ff", "version_major": 2, "version_minor": 0 }, @@ -6051,7 +6495,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "db31f3721f9d4bb68940c4b68363ae91", "version_major": 2, "version_minor": 0 }, @@ -6065,7 +6509,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "3f3cfeef6c994126bb25ef544e10d3e2", "version_major": 2, "version_minor": 0 }, @@ -6079,7 +6523,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "77450f9684cd42ac9ebc992dd86590b7", "version_major": 2, "version_minor": 0 }, @@ -6093,7 +6537,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "0d41009181074ffcbc2b81fd23a5e486", "version_major": 2, "version_minor": 0 }, @@ -6107,7 +6551,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "86ad33abbff742d3a66014271d575da4", "version_major": 2, "version_minor": 0 }, @@ -6121,7 +6565,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "170aa9e0a58c449f97cc5d210cba04a9", "version_major": 2, "version_minor": 0 }, @@ -6135,7 +6579,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6357463ea1464043b3aaa481d08ec4dc", "version_major": 2, "version_minor": 0 }, @@ -6149,7 +6593,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "c67de6d754454a28a4eb9da14cfb9b45", "version_major": 2, "version_minor": 0 }, @@ -6163,7 +6607,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "226a8c4a25194a5597a56915a9a203e6", "version_major": 2, "version_minor": 0 }, @@ -6177,7 +6621,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "ab01bdbc450a426b8766f783bb71eb17", "version_major": 2, "version_minor": 0 }, @@ -6191,7 +6635,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2e7f5f09bce94cdd808251b783ff7195", "version_major": 2, "version_minor": 0 }, @@ -6205,7 +6649,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "077790461d6a43db8c785be6c6f2f71b", "version_major": 2, "version_minor": 0 }, @@ -6219,7 +6663,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "7782347a868a4cb09ad084e0593fc921", "version_major": 2, "version_minor": 0 }, @@ -6233,7 +6677,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "46a12e5ee12547f5864399e6f3cdd08b", "version_major": 2, "version_minor": 0 }, @@ -6247,7 +6691,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6cc156f0ca724500b25ebd9e8f9e11df", "version_major": 2, "version_minor": 0 }, @@ -6261,7 +6705,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "02c4f52ca3784311886f0616648ece3f", "version_major": 2, "version_minor": 0 }, @@ -6275,7 +6719,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "9ef5b02f482344818b2aeb04cc8ff5bf", "version_major": 2, "version_minor": 0 }, @@ -6289,7 +6733,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "2ed0de87ff334b7396d54733b8cb942c", "version_major": 2, "version_minor": 0 }, @@ -6303,7 +6747,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "710efb8aace342a48ef6148920857667", "version_major": 2, "version_minor": 0 }, @@ -6317,7 +6761,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "491665fa6ec546cc87dfe5f3ad1858f5", "version_major": 2, "version_minor": 0 }, @@ -6331,7 +6775,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:24.382775Z", + "iopub.status.busy": "2023-08-22T06:44:24.382660Z", + "iopub.status.idle": "2023-08-22T06:44:24.491042Z", + "shell.execute_reply": "2023-08-22T06:44:24.490723Z" + } + }, "outputs": [], "source": [ "((X_train, Y_train),\n", @@ -6486,6 +6930,12 @@ "execution_count": 79, "id": "e2a88e57", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:24.492855Z", + "iopub.status.busy": "2023-08-22T06:44:24.492752Z", + "iopub.status.idle": "2023-08-22T06:44:24.497193Z", + "shell.execute_reply": "2023-08-22T06:44:24.496916Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -6511,6 +6961,12 @@ "execution_count": 80, "id": "9a3cf7a3", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:24.498888Z", + "iopub.status.busy": "2023-08-22T06:44:24.498790Z", + "iopub.status.idle": "2023-08-22T06:44:24.500775Z", + "shell.execute_reply": "2023-08-22T06:44:24.500516Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -6534,7 +6990,14 @@ "cell_type": "code", "execution_count": 81, "id": "b46f02c2", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:24.502208Z", + "iopub.status.busy": "2023-08-22T06:44:24.502114Z", + "iopub.status.idle": "2023-08-22T06:44:43.491232Z", + "shell.execute_reply": "2023-08-22T06:44:43.490911Z" + } + }, "outputs": [], "source": [ "coefs = []\n", @@ -6561,6 +7024,12 @@ "execution_count": 82, "id": "e5fb6afa", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:43.492990Z", + "iopub.status.busy": "2023-08-22T06:44:43.492881Z", + "iopub.status.idle": "2023-08-22T06:44:43.495136Z", + "shell.execute_reply": "2023-08-22T06:44:43.494898Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -6583,6 +7052,12 @@ "execution_count": 83, "id": "cad28f1a", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:43.496615Z", + "iopub.status.busy": "2023-08-22T06:44:43.496512Z", + "iopub.status.idle": "2023-08-22T06:44:43.817649Z", + "shell.execute_reply": "2023-08-22T06:44:43.804167Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -6626,6 +7101,12 @@ "execution_count": 84, "id": "a66ecdd8", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:43.829522Z", + "iopub.status.busy": "2023-08-22T06:44:43.829394Z", + "iopub.status.idle": "2023-08-22T06:44:43.970259Z", + "shell.execute_reply": "2023-08-22T06:44:43.969880Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -6673,6 +7154,12 @@ "execution_count": 85, "id": "62440c1c", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:43.972104Z", + "iopub.status.busy": "2023-08-22T06:44:43.971981Z", + "iopub.status.idle": "2023-08-22T06:44:43.973750Z", + "shell.execute_reply": "2023-08-22T06:44:43.973468Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -6718,7 +7205,14 @@ "cell_type": "code", "execution_count": 86, "id": "c73d6e28", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:43.975482Z", + "iopub.status.busy": "2023-08-22T06:44:43.975361Z", + "iopub.status.idle": "2023-08-22T06:44:43.977735Z", + "shell.execute_reply": "2023-08-22T06:44:43.977461Z" + } + }, "outputs": [], "source": [ "imdb_seq_dm = SimpleDataModule(imdb_seq_train,\n", @@ -6762,6 +7256,12 @@ "execution_count": 87, "id": "cc9bbd00", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:43.979333Z", + "iopub.status.busy": "2023-08-22T06:44:43.979219Z", + "iopub.status.idle": "2023-08-22T06:44:43.981720Z", + "shell.execute_reply": "2023-08-22T06:44:43.981436Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -6792,7 +7292,14 @@ "cell_type": "code", "execution_count": 88, "id": "5c9ffb46", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:43.983706Z", + "iopub.status.busy": "2023-08-22T06:44:43.983605Z", + "iopub.status.idle": "2023-08-22T06:44:44.006133Z", + "shell.execute_reply": "2023-08-22T06:44:44.005857Z" + } + }, "outputs": [ { "data": { @@ -6808,7 +7315,7 @@ "Total params: 328,577\n", "Trainable params: 328,577\n", "Non-trainable params: 0\n", - "Total mult-adds (Units.MEGABYTES): 45.44\n", + "Total mult-adds (M): 45.44\n", "===================================================================================================================\n", "Input size (MB): 50.00\n", "Forward/backward pass size (MB): 2.56\n", @@ -6844,7 +7351,14 @@ "cell_type": "code", "execution_count": 89, "id": "a2d6ddfd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:44.007842Z", + "iopub.status.busy": "2023-08-22T06:44:44.007718Z", + "iopub.status.idle": "2023-08-22T06:44:44.010586Z", + "shell.execute_reply": "2023-08-22T06:44:44.010323Z" + } + }, "outputs": [], "source": [ "lstm_module = SimpleModule.binary_classification(lstm_model)\n", @@ -6856,6 +7370,12 @@ "execution_count": 90, "id": "1d76f970", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:44:44.012129Z", + "iopub.status.busy": "2023-08-22T06:44:44.012007Z", + "iopub.status.idle": "2023-08-22T06:57:52.592058Z", + "shell.execute_reply": "2023-08-22T06:57:52.591679Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -6882,7 +7402,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "378b19c7eac744308f7e66510ed91e53", "version_major": 2, "version_minor": 0 }, @@ -6896,7 +7416,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e24a4171456b412db47d6577cb336c37", + "model_id": "ef5ef897fb0f445d8bdbb9603ab9fe1f", "version_major": 2, "version_minor": 0 }, @@ -6910,7 +7430,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a366613bbed148f99faf7c3048fd5342", "version_major": 2, "version_minor": 0 }, @@ -6924,7 +7444,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "33c8cd8f47a44e5a86e58f83612915ab", "version_major": 2, "version_minor": 0 }, @@ -6938,7 +7458,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "44ba1553a3f54642b05ce7524df82025", "version_major": 2, "version_minor": 0 }, @@ -6952,7 +7472,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "fe4ad23d277b45bd9ac5e6b01e8a755a", "version_major": 2, "version_minor": 0 }, @@ -6966,7 +7486,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "5d76d21f617146b19033d03068811bae", "version_major": 2, "version_minor": 0 }, @@ -6980,7 +7500,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "1a61a4d5b3f64c85b9d36656ab359beb", "version_major": 2, "version_minor": 0 }, @@ -6994,7 +7514,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e25cb2f8f14c4b6099c1a1b37e89ed54", "version_major": 2, "version_minor": 0 }, @@ -7008,7 +7528,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a74e862dfa64467ca538e1decb34ebd6", "version_major": 2, "version_minor": 0 }, @@ -7022,7 +7542,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6d3dd1f4233a4e779585e1be6aee8943", "version_major": 2, "version_minor": 0 }, @@ -7036,7 +7556,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "6c69d1606f5e43d399996acdd4b275fe", "version_major": 2, "version_minor": 0 }, @@ -7050,7 +7570,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "652b9712efa04ab2a0444464fb7aef43", "version_major": 2, "version_minor": 0 }, @@ -7064,7 +7584,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "8a69f0865eec43b5bab4d01c62ccc2dc", "version_major": 2, "version_minor": 0 }, @@ -7078,7 +7598,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "56fd72140fdf48fe87011e828715d038", "version_major": 2, "version_minor": 0 }, @@ -7092,7 +7612,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "aaad6e2fd6ff44aa9e7842830f2c31c5", "version_major": 2, "version_minor": 0 }, @@ -7106,7 +7626,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "3eee2abff98b4e1080d1ade699034ec0", "version_major": 2, "version_minor": 0 }, @@ -7120,7 +7640,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "1d18c86360cd4e32b69f20bb667f41ad", "version_major": 2, "version_minor": 0 }, @@ -7134,7 +7654,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "e100cb13de18446f85a7d9864b04fbbf", "version_major": 2, "version_minor": 0 }, @@ -7148,7 +7668,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "b22c18caa3c945ec9b5ea2427c8b76ad", "version_major": 2, "version_minor": 0 }, @@ -7162,7 +7682,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "a3282157e73a4b6ba710cd530f2911e6", "version_major": 2, "version_minor": 0 }, @@ -7176,7 +7696,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "56a98588414e4d21a98f337a9121e943", "version_major": 2, "version_minor": 0 }, @@ -7217,12 +7737,19 @@ "cell_type": "code", "execution_count": 91, "id": "d8a60d35", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:52.595337Z", + "iopub.status.busy": "2023-08-22T06:57:52.595214Z", + "iopub.status.idle": "2023-08-22T06:57:55.788029Z", + "shell.execute_reply": "2023-08-22T06:57:55.787703Z" + } + }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4e512ef8a7d348eea7f423a9ed96c86e", + "model_id": "295ea7acd6904c2bac76f95a99e0e77e", "version_major": 2, "version_minor": 0 }, @@ -7234,34 +7761,21 @@ "output_type": "display_data" }, { - "data": { - "text/html": [ - "
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.8549200296401978\n", + " test_loss 0.7031683325767517\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] }, { "data": { "text/plain": [ - "[{'test_loss': 0.7677657604217529, 'test_accuracy': 0.8480799794197083}]" + "[{'test_loss': 0.7031683325767517, 'test_accuracy': 0.8549200296401978}]" ] }, "execution_count": 91, @@ -7286,6 +7800,12 @@ "execution_count": 92, "id": "65d7276c", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:55.789720Z", + "iopub.status.busy": "2023-08-22T06:57:55.789606Z", + "iopub.status.idle": "2023-08-22T06:57:55.884354Z", + "shell.execute_reply": "2023-08-22T06:57:55.882420Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -7301,7 +7821,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -7327,6 +7847,12 @@ "execution_count": 93, "id": "c6f2d6c4", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:55.889361Z", + "iopub.status.busy": "2023-08-22T06:57:55.889040Z", + "iopub.status.idle": "2023-08-22T06:57:55.894226Z", + "shell.execute_reply": "2023-08-22T06:57:55.893514Z" + }, "lines_to_next_cell": 2 }, "outputs": [], @@ -7354,7 +7880,14 @@ "cell_type": "code", "execution_count": 94, "id": "f3e17682", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:55.899236Z", + "iopub.status.busy": "2023-08-22T06:57:55.898584Z", + "iopub.status.idle": "2023-08-22T06:57:55.919645Z", + "shell.execute_reply": "2023-08-22T06:57:55.918698Z" + } + }, "outputs": [], "source": [ "NYSE = load_data('NYSE')\n", @@ -7379,7 +7912,14 @@ "cell_type": "code", "execution_count": 95, "id": "78707eda", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:55.924653Z", + "iopub.status.busy": "2023-08-22T06:57:55.924212Z", + "iopub.status.idle": "2023-08-22T06:57:55.941488Z", + "shell.execute_reply": "2023-08-22T06:57:55.940914Z" + } + }, "outputs": [], "source": [ "for lag in range(1, 6):\n", @@ -7405,6 +7945,12 @@ "execution_count": 96, "id": "4d894824", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:55.946619Z", + "iopub.status.busy": "2023-08-22T06:57:55.946232Z", + "iopub.status.idle": "2023-08-22T06:57:55.957091Z", + "shell.execute_reply": "2023-08-22T06:57:55.956345Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -7442,7 +7988,14 @@ "cell_type": "code", "execution_count": 97, "id": "4d7f5ce0", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:55.961811Z", + "iopub.status.busy": "2023-08-22T06:57:55.961399Z", + "iopub.status.idle": "2023-08-22T06:57:56.102250Z", + "shell.execute_reply": "2023-08-22T06:57:56.100744Z" + } + }, "outputs": [ { "data": { @@ -7476,6 +8029,12 @@ "execution_count": 98, "id": "a6b371bb", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.107157Z", + "iopub.status.busy": "2023-08-22T06:57:56.106829Z", + "iopub.status.idle": "2023-08-22T06:57:56.120860Z", + "shell.execute_reply": "2023-08-22T06:57:56.120304Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -7500,6 +8059,12 @@ "execution_count": 99, "id": "a2a8cc85", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.124686Z", + "iopub.status.busy": "2023-08-22T06:57:56.124536Z", + "iopub.status.idle": "2023-08-22T06:57:56.148874Z", + "shell.execute_reply": "2023-08-22T06:57:56.148350Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -7552,6 +8117,12 @@ "execution_count": 100, "id": "8ee6e6a3", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.154237Z", + "iopub.status.busy": "2023-08-22T06:57:56.153271Z", + "iopub.status.idle": "2023-08-22T06:57:56.164612Z", + "shell.execute_reply": "2023-08-22T06:57:56.162355Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -7592,6 +8163,12 @@ "execution_count": 101, "id": "d35ceb54", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.168885Z", + "iopub.status.busy": "2023-08-22T06:57:56.168313Z", + "iopub.status.idle": "2023-08-22T06:57:56.177028Z", + "shell.execute_reply": "2023-08-22T06:57:56.174770Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -7629,7 +8206,14 @@ "cell_type": "code", "execution_count": 102, "id": "9e3dc6d5", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.180875Z", + "iopub.status.busy": "2023-08-22T06:57:56.180571Z", + "iopub.status.idle": "2023-08-22T06:57:56.188877Z", + "shell.execute_reply": "2023-08-22T06:57:56.188074Z" + } + }, "outputs": [], "source": [ "class NYSEModel(nn.Module):\n", @@ -7666,7 +8250,14 @@ "cell_type": "code", "execution_count": 103, "id": "df5e5ab6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.192921Z", + "iopub.status.busy": "2023-08-22T06:57:56.192198Z", + "iopub.status.idle": "2023-08-22T06:57:56.205860Z", + "shell.execute_reply": "2023-08-22T06:57:56.203593Z" + } + }, "outputs": [], "source": [ "datasets = []\n", @@ -7690,6 +8281,12 @@ "execution_count": 104, "id": "d7f49bec", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.209732Z", + "iopub.status.busy": "2023-08-22T06:57:56.209439Z", + "iopub.status.idle": "2023-08-22T06:57:56.224094Z", + "shell.execute_reply": "2023-08-22T06:57:56.221572Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -7707,7 +8304,7 @@ "Total params: 217\n", "Trainable params: 217\n", "Non-trainable params: 0\n", - "Total mult-adds (Units.MEGABYTES): 1.83\n", + "Total mult-adds (M): 1.83\n", "===================================================================================================================\n", "Input size (MB): 0.11\n", "Forward/backward pass size (MB): 0.86\n", @@ -7743,6 +8340,12 @@ "execution_count": 105, "id": "ea7ce0f4", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.228022Z", + "iopub.status.busy": "2023-08-22T06:57:56.227434Z", + "iopub.status.idle": "2023-08-22T06:57:56.236333Z", + "shell.execute_reply": "2023-08-22T06:57:56.234101Z" + }, "lines_to_next_cell": 0 }, "outputs": [], @@ -7766,7 +8369,14 @@ "cell_type": "code", "execution_count": 106, "id": "ccd77738", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:56.242815Z", + "iopub.status.busy": "2023-08-22T06:57:56.242259Z", + "iopub.status.idle": "2023-08-22T06:57:57.426177Z", + "shell.execute_reply": "2023-08-22T06:57:57.425792Z" + } + }, "outputs": [ { "name": "stdout", @@ -7800,7 +8410,14 @@ "cell_type": "code", "execution_count": 107, "id": "96e04e3f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:57.428165Z", + "iopub.status.busy": "2023-08-22T06:57:57.428026Z", + "iopub.status.idle": "2023-08-22T06:57:57.431868Z", + "shell.execute_reply": "2023-08-22T06:57:57.431594Z" + } + }, "outputs": [], "source": [ "nyse_optimizer = RMSprop(nyse_model.parameters(),\n", @@ -7824,6 +8441,12 @@ "execution_count": 108, "id": "fc6ba2ca", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T06:57:57.433417Z", + "iopub.status.busy": "2023-08-22T06:57:57.433307Z", + "iopub.status.idle": "2023-08-22T07:00:40.275665Z", + "shell.execute_reply": "2023-08-22T07:00:40.275338Z" + }, "lines_to_next_cell": 2 }, "outputs": [ @@ -7850,7 +8473,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "95b6a3cd420043e6b2a2ee4b5c581226", "version_major": 2, "version_minor": 0 }, @@ -7864,7 +8487,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd881313814b4b319883de36a456b21e", + "model_id": "bbdc8300574e444f84013f84c05e6988", "version_major": 2, "version_minor": 0 }, @@ -7878,7 +8501,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "96302a61558d40549b7802f4c44263eb", + "model_id": "38755c7422b34a949cc732c06b1e682e", "version_major": 2, "version_minor": 0 }, @@ -9194,7 +9817,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb7dd989673a4223b66345ef77406828", + "model_id": "0ac4cea1d60b4f79bc3b45b07c38ade8", "version_major": 2, "version_minor": 0 }, @@ -9208,7 +9831,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e74c6d1b7c64e4596a655a0fbaf1bfb", + "model_id": "758f258102814532b42dda1f53b094a5", "version_major": 2, "version_minor": 0 }, @@ -9222,7 +9845,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d716d0b7820d475ca7653959b52d5450", + "model_id": "8e85659e3769441bae9ba746d0e525ad", "version_major": 2, "version_minor": 0 }, @@ -9236,7 +9859,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "137da5f57ba84efdb51aff612b72db01", + "model_id": "486ae51d02924739bc6a5ca3b3e6d509", "version_major": 2, "version_minor": 0 }, @@ -9250,7 +9873,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd227089abe34ebda36b2d18df87a86a", + "model_id": "8354aab430d94e518cd1d3e94911337f", "version_major": 2, "version_minor": 0 }, @@ -9264,7 +9887,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "044b5721d12d4dadbc68f2e1ef6db71a", + "model_id": "511a0ae8aad344a69d9e566c023fa35e", "version_major": 2, "version_minor": 0 }, @@ -9278,7 +9901,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc86818b06a740479b3157b29a1cb18e", + "model_id": "13c285c6471046cbac5ff81f93657c26", "version_major": 2, "version_minor": 0 }, @@ -9292,7 +9915,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd634e6fc883498cadcb00911c514371", + "model_id": "977ae5e76c5d44b5b41e8afa239a643d", "version_major": 2, "version_minor": 0 }, @@ -9306,7 +9929,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3a52574849754ad5b349a2ef52a33722", + "model_id": "bf792c29044d4c38b76f2ad756e03da7", "version_major": 2, "version_minor": 0 }, @@ -9320,7 +9943,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35f1a57c0f6147ce81b036c866a17275", + "model_id": "ce92f452f9314a93afafc2032d615c2d", "version_major": 2, "version_minor": 0 }, @@ -9334,7 +9957,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3cb251ea1f6a4ae7a87fb959e362d392", + "model_id": "279186f508234c3dbbcc710426a1c29e", "version_major": 2, "version_minor": 0 }, @@ -9348,7 +9971,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4e12dfd333064cf5a1fffa8ab56f67a6", + "model_id": "2a4e6adc03ec43bdbca612265317da94", "version_major": 2, "version_minor": 0 }, @@ -9362,7 +9985,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73e3283065a94da9b0de1b38f87eb801", + "model_id": "cc23610a2bff44898bccc8b2b32ba273", "version_major": 2, "version_minor": 0 }, @@ -9376,7 +9999,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2755e57697a4a01b6514fdba5578bec", + "model_id": "04dda1d230f74d89a3ee7a5b5d98e726", "version_major": 2, "version_minor": 0 }, @@ -9390,7 +10013,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8f3787f489f46959322dac484b7ed93", + "model_id": "a654c1113f08483b812944094977a640", "version_major": 2, "version_minor": 0 }, @@ -9404,7 +10027,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f24824825bf644ae831c0e922e16a6c7", + "model_id": "27e31a821ce443eb9009167b3c80f4d8", "version_major": 2, "version_minor": 0 }, @@ -9418,7 +10041,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9abde42fb6564285aef92cdc78ac08a4", + "model_id": "d6aa53936e6544fa81fc40477ff0eb36", "version_major": 2, "version_minor": 0 }, @@ -9432,7 +10055,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18ab584d1482478b9b7d34e90db79dd1", + "model_id": "74754a163a0248289e40025c911edda2", "version_major": 2, "version_minor": 0 }, @@ -9446,7 +10069,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7b030704b1b545fcad5ee48c27c6b90c", + "model_id": "8c5a887f0b6849038b79dd085b72acf8", "version_major": 2, "version_minor": 0 }, @@ -9460,7 +10083,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c47f07fcd58847b1b24f383546eb653c", + "model_id": "3b02d7e8b4f643c3b4e80b92be4df5a9", "version_major": 2, "version_minor": 0 }, @@ -9474,7 +10097,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f99c0e8c4b65436dafa5c2a9a5452e06", + "model_id": "246618accc3e4cf781e83c608fe100ee", "version_major": 2, "version_minor": 0 }, @@ -9488,7 +10111,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24f347e1c972488ab6d64bcb07757238", + "model_id": "155223107da644d2b0f7e8d7345de660", "version_major": 2, "version_minor": 0 }, @@ -9502,7 +10125,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc6b7ff2a86c41cca3a9b423c0297b69", + "model_id": "7d395c5566d740b5b86b9b59bd34deaf", "version_major": 2, "version_minor": 0 }, @@ -9516,7 +10139,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eeff56e6de874ca3bbacdf08fbcead78", + "model_id": "300178e2115441d5a86263fac06833fb", "version_major": 2, "version_minor": 0 }, @@ -9530,7 +10153,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "bdf0d51cecf64b6b88b2189e61e2697d", + "model_id": "d91a8979aa8e4372881dfa4c1d2d0fb8", "version_major": 2, "version_minor": 0 }, @@ -9964,7 +10587,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3663a9cc5dd6492c838b8856ee3db172", + "model_id": "776e0ceb423947fab047fa0a36f1a649", "version_major": 2, "version_minor": 0 }, @@ -9978,7 +10601,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5b8a90932c5c43449baddb012d3f8d2f", + "model_id": "a5939de5d6e2412bba3a5378ff52098f", "version_major": 2, "version_minor": 0 }, @@ -9992,7 +10615,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "acfe2fcbcbeb40b689a942d4a384e81e", + "model_id": "1cb2ef792e7246e78aca5c23ad00754c", "version_major": 2, "version_minor": 0 }, @@ -10006,7 +10629,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a41e9df7b5be41f680c2486bb036eac0", + "model_id": "8577b08dfb83408daa431ebe138d0803", "version_major": 2, "version_minor": 0 }, @@ -10020,7 +10643,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "70cfd7e68ad546fca7b8b4fcb5fe112a", + "model_id": "081e3021531141e48f1cde90a2e60671", "version_major": 2, "version_minor": 0 }, @@ -10034,7 +10657,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc6d28f7b6674cc78526f2a04a232f87", + "model_id": "04da4e8a94b8485f9d4f5c355005935f", "version_major": 2, "version_minor": 0 }, @@ -10048,7 +10671,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae26f55dfe5d44cd9fae433fb8a3ed46", + "model_id": "f4615440ddf24df0a05fc9ff41997f68", "version_major": 2, "version_minor": 0 }, @@ -10062,7 +10685,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b61738f2417244b6b7df1ebb3b3f24d7", + "model_id": "9752977f30eb4c12b2ac52db90ffe806", "version_major": 2, "version_minor": 0 }, @@ -10076,7 +10699,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fdbf1b51bbd54898b58a2de9f695d5ba", + "model_id": "dfa59627c528419aae74ac648e4699c2", "version_major": 2, "version_minor": 0 }, @@ -10090,7 +10713,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d438be1b459e4f0f90e1a26704baa0f1", + "model_id": "626f5be4b8a34439893f6a6bc8045c2c", "version_major": 2, "version_minor": 0 }, @@ -10104,7 +10727,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "164a559d88cd48c58bb43f99cf5819fb", + "model_id": "78123190b7e2489f89d7e56af80f09ca", "version_major": 2, "version_minor": 0 }, @@ -10118,7 +10741,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e142ec45c48e400bbf4cf7dbc8b94e39", + "model_id": "f5d88208fe1f40d79db43eeb3703df18", "version_major": 2, "version_minor": 0 }, @@ -10132,7 +10755,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b98239d615c44f9280cd53bf430b38ef", + "model_id": "74f80b2748054370947e20aef204d640", "version_major": 2, "version_minor": 0 }, @@ -10146,7 +10769,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee3012ec548c4468ab19394fcc61dfe1", + "model_id": "276324d347834f9fbf90e8acf87776e2", "version_major": 2, "version_minor": 0 }, @@ -10160,7 +10783,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "59abff92475945148512c124b31620ae", + "model_id": "202c78ca5dfc4456adc95c21f0868eee", "version_major": 2, "version_minor": 0 }, @@ -10174,7 +10797,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10742df4535742c8acdcdbea8d8d6a02", + "model_id": "e9ae73c46ec4448a91b1a66a6212c0c1", "version_major": 2, "version_minor": 0 }, @@ -10188,7 +10811,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e3d33d2a59f4e2c8413b43cfaee41d1", + "model_id": "fe1f482f4b1b4f47a56a4e97439842b9", "version_major": 2, "version_minor": 0 }, @@ -10202,7 +10825,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fd03ae4594b4574a55f06d248d8b61a", + "model_id": "7a4b6820418b41faa3d24c135b7fe5ca", "version_major": 2, "version_minor": 0 }, @@ -10216,7 +10839,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "976c6fee189c41b4b5445895fb180c8d", + "model_id": "274c8f91da714f279b9995aa22fc656d", "version_major": 2, "version_minor": 0 }, @@ -10230,7 +10853,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "198e1fa4f5124895bc0ee188984ea726", + "model_id": "1fd9417afaaf423fb91abd1bc89e082c", "version_major": 2, "version_minor": 0 }, @@ -10244,7 +10867,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c78ca1847595417aa71358a1c1b1a4b0", + "model_id": "69162f7af7d446e9892f3865f6f3c204", "version_major": 2, "version_minor": 0 }, @@ -10258,7 +10881,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af7a5a5a341348f2a0be2f3e77656549", + "model_id": "ce52107f794845beb559296b8df3ca4c", "version_major": 2, "version_minor": 0 }, @@ -10272,7 +10895,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b5ab2cc2a47b42828c7a6780a6db2e00", + "model_id": "039eb58619a04a919785b454d4e75d97", "version_major": 2, "version_minor": 0 }, @@ -10286,7 +10909,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f972c01912c545948b56ea640af2c5b0", + "model_id": "09fd8d108d384f8a81c39661d780d169", "version_major": 2, "version_minor": 0 }, @@ -10300,7 +10923,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "213b3d4ee66d42e1b57a82d35b720112", + "model_id": "25d997f386cf40abb7e1cb6d3ccc492d", "version_major": 2, "version_minor": 0 }, @@ -10314,7 +10937,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddfc7dcdd1c94636929318d51a61e048", + "model_id": "576f1dfdbc12467ebe290351d695c9bb", "version_major": 2, "version_minor": 0 }, @@ -10328,7 +10951,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fdce7be7f074ec589421caa0319862f", + "model_id": "f3fb45ebffc4481b98ed10f50a0d45e9", "version_major": 2, "version_minor": 0 }, @@ -10342,7 +10965,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "180334cb0f4c4cc6a5f2eeedc55a986b", + "model_id": "864efaae9ebb4a2cb140e89fb49c4b1a", "version_major": 2, "version_minor": 0 }, @@ -10356,7 +10979,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2e9e0572d4c48bd9dcf25960be64cde", + "model_id": "ca0d6a01341a41cc84ffacaa43f9ab29", "version_major": 2, "version_minor": 0 }, @@ -10370,7 +10993,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e26b9ff0a7642e0b8400f0a937eeb30", + "model_id": "cb072b8b7e7f4823acd69e8090be31cf", "version_major": 2, "version_minor": 0 }, @@ -10384,7 +11007,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf9537d5986b4aacad134c66a4242b0e", + "model_id": "777feac25bc24e619ecbc60c835c124a", "version_major": 2, "version_minor": 0 }, @@ -10398,7 +11021,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e4ab38c6e45d4628b56bbe5dac9458d7", + "model_id": "f89f565d1e07430d97c568d3185a3fcf", "version_major": 2, "version_minor": 0 }, @@ -10412,7 +11035,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18e800771f6f439bb9f15de7edaf8274", + "model_id": "740d69a62cef481eae149694f26e2d7d", "version_major": 2, "version_minor": 0 }, @@ -10426,7 +11049,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee30326da017481f94e780628f0ec74d", + "model_id": "781a1d7dd346434cb361755233d1c625", "version_major": 2, "version_minor": 0 }, @@ -10440,7 +11063,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "f01094a315c4420e9aba161bd526a1e0", + "model_id": "994179ad8f7f4d65adedba46549b8b8f", "version_major": 2, "version_minor": 0 }, @@ -10524,7 +11147,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "652fb20eb89245a8ac16173eeb553633", + "model_id": "a867c1a31814435b985a017541c07a85", "version_major": 2, "version_minor": 0 }, @@ -10538,7 +11161,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "92757d397a9944e9bf89a627d34d670a", + "model_id": "6e404ac3b2ae4a98b97e1029acf9efc9", "version_major": 2, "version_minor": 0 }, @@ -10552,7 +11175,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d4b73d5c186347d9a05f2486c5baafee", + "model_id": "9b55e6ee243d4823bd15fb8f2f495158", "version_major": 2, "version_minor": 0 }, @@ -10566,7 +11189,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4daeb823d61246a0aa572689807c117d", + "model_id": "e2e7bb6a1da84590b41f8ea9ffb59da6", "version_major": 2, "version_minor": 0 }, @@ -10580,7 +11203,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4da85fb13a6241efa01e2e144209cd5f", + "model_id": "af546f5012484bc5a061a81469ad52b8", "version_major": 2, "version_minor": 0 }, @@ -10594,7 +11217,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6f4c663a0b6449828fcacf1d77b0fad1", + "model_id": "20887ceec3994c03ae2c5c2e3e1a02d6", "version_major": 2, "version_minor": 0 }, @@ -10608,7 +11231,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ada51e9d1ee84f7c8d7cda87a9fed382", + "model_id": "fd444c98f6e74543803b4bbec3706c9d", "version_major": 2, "version_minor": 0 }, @@ -10622,7 +11245,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "035517fa34c84edb8520548a6a6f5f2f", + "model_id": "acab03215fc748008c9ddf873de20a5b", "version_major": 2, "version_minor": 0 }, @@ -10636,7 +11259,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddc0d23180304537a5d5a408daed4159", + "model_id": "6c01327034f34540830e0810fc97d553", "version_major": 2, "version_minor": 0 }, @@ -10650,7 +11273,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T07:00:40.293426Z", + "iopub.status.busy": "2023-08-22T07:00:40.293319Z", + "iopub.status.idle": "2023-08-22T07:00:40.296658Z", + "shell.execute_reply": "2023-08-22T07:00:40.296393Z" + } + }, "outputs": [], "source": [ "nl_model = NonLinearARModel()\n", @@ -10852,6 +11490,12 @@ "execution_count": 113, "id": "9fc113a1", "metadata": { + "execution": { + "iopub.execute_input": "2023-08-22T07:00:40.298197Z", + "iopub.status.busy": "2023-08-22T07:00:40.298117Z", + "iopub.status.idle": "2023-08-22T07:00:54.837234Z", + "shell.execute_reply": "2023-08-22T07:00:54.836928Z" + }, "lines_to_next_cell": 0 }, "outputs": [ @@ -10878,7 +11522,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "", + "model_id": "60ce6a3ddb494c16a8b3681ec8a1a4b4", "version_major": 2, "version_minor": 0 }, @@ -10892,7 +11536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6327380ca56a49628cb311d7382fe96d", + "model_id": "3bda25cffd7d41ac8d2636e841039865", "version_major": 2, "version_minor": 0 }, @@ -10906,7 +11550,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:05.847658Z", - "iopub.status.busy": "2023-08-21T02:30:05.847519Z", - "iopub.status.idle": "2023-08-21T02:30:05.901295Z", - "shell.execute_reply": "2023-08-21T02:30:05.900935Z" + "iopub.execute_input": "2023-08-22T07:00:51.778810Z", + "iopub.status.busy": "2023-08-22T07:00:51.778673Z", + "iopub.status.idle": "2023-08-22T07:00:51.828367Z", + "shell.execute_reply": "2023-08-22T07:00:51.827978Z" }, "lines_to_next_cell": 2 }, @@ -479,10 +479,10 @@ "id": "5f9303dd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:05.903263Z", - "iopub.status.busy": "2023-08-21T02:30:05.903017Z", - "iopub.status.idle": "2023-08-21T02:30:05.930691Z", - "shell.execute_reply": "2023-08-21T02:30:05.930331Z" + "iopub.execute_input": "2023-08-22T07:00:51.830679Z", + "iopub.status.busy": "2023-08-22T07:00:51.830375Z", + "iopub.status.idle": "2023-08-22T07:00:51.855011Z", + "shell.execute_reply": 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"2023-08-22T07:00:51.902771Z", + "shell.execute_reply": "2023-08-22T07:00:51.902284Z" } }, "outputs": [ @@ -826,10 +826,10 @@ "id": "8d999f26", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:05.981441Z", - "iopub.status.busy": "2023-08-21T02:30:05.981315Z", - "iopub.status.idle": "2023-08-21T02:30:05.986317Z", - "shell.execute_reply": "2023-08-21T02:30:05.985949Z" + "iopub.execute_input": "2023-08-22T07:00:51.904821Z", + "iopub.status.busy": "2023-08-22T07:00:51.904683Z", + "iopub.status.idle": "2023-08-22T07:00:51.908678Z", + "shell.execute_reply": "2023-08-22T07:00:51.908230Z" } }, "outputs": [], @@ -859,10 +859,10 @@ "id": "a1f6b355", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:05.988012Z", - "iopub.status.busy": "2023-08-21T02:30:05.987898Z", - "iopub.status.idle": "2023-08-21T02:30:05.993889Z", - "shell.execute_reply": "2023-08-21T02:30:05.993534Z" + "iopub.execute_input": "2023-08-22T07:00:51.910559Z", + "iopub.status.busy": "2023-08-22T07:00:51.910451Z", + "iopub.status.idle": "2023-08-22T07:00:51.916316Z", + "shell.execute_reply": "2023-08-22T07:00:51.915990Z" } }, "outputs": [ @@ -987,10 +987,10 @@ "id": "a1a9d5b3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:05.995682Z", - "iopub.status.busy": "2023-08-21T02:30:05.995549Z", - "iopub.status.idle": "2023-08-21T02:30:06.005479Z", - "shell.execute_reply": "2023-08-21T02:30:06.005089Z" + "iopub.execute_input": "2023-08-22T07:00:51.918281Z", + "iopub.status.busy": "2023-08-22T07:00:51.918144Z", + "iopub.status.idle": "2023-08-22T07:00:51.927845Z", + "shell.execute_reply": "2023-08-22T07:00:51.927490Z" } }, "outputs": [ @@ -1129,10 +1129,10 @@ "id": "1a18b56a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.007172Z", - "iopub.status.busy": "2023-08-21T02:30:06.007049Z", - "iopub.status.idle": "2023-08-21T02:30:06.014185Z", - "shell.execute_reply": "2023-08-21T02:30:06.013870Z" + "iopub.execute_input": "2023-08-22T07:00:51.929658Z", + "iopub.status.busy": "2023-08-22T07:00:51.929522Z", + "iopub.status.idle": "2023-08-22T07:00:51.936112Z", + "shell.execute_reply": "2023-08-22T07:00:51.935793Z" }, "lines_to_next_cell": 0 }, @@ -1290,10 +1290,10 @@ "id": "ff3de29c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.015778Z", - "iopub.status.busy": "2023-08-21T02:30:06.015664Z", - "iopub.status.idle": "2023-08-21T02:30:06.124035Z", - "shell.execute_reply": "2023-08-21T02:30:06.123732Z" + "iopub.execute_input": "2023-08-22T07:00:51.937659Z", + "iopub.status.busy": "2023-08-22T07:00:51.937539Z", + "iopub.status.idle": "2023-08-22T07:00:52.033442Z", + "shell.execute_reply": "2023-08-22T07:00:52.032997Z" }, "lines_to_next_cell": 2 }, @@ -1333,10 +1333,10 @@ "id": "cd9060c1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.125714Z", - "iopub.status.busy": "2023-08-21T02:30:06.125592Z", - "iopub.status.idle": "2023-08-21T02:30:06.243701Z", - "shell.execute_reply": "2023-08-21T02:30:06.243300Z" + "iopub.execute_input": "2023-08-22T07:00:52.035515Z", + "iopub.status.busy": "2023-08-22T07:00:52.035365Z", + "iopub.status.idle": "2023-08-22T07:00:52.186180Z", + "shell.execute_reply": "2023-08-22T07:00:52.185871Z" } }, "outputs": [ @@ -1378,10 +1378,10 @@ "id": "6af7106e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.245493Z", - "iopub.status.busy": "2023-08-21T02:30:06.245357Z", - "iopub.status.idle": "2023-08-21T02:30:06.281521Z", - "shell.execute_reply": "2023-08-21T02:30:06.281138Z" + "iopub.execute_input": "2023-08-22T07:00:52.188568Z", + "iopub.status.busy": "2023-08-22T07:00:52.188400Z", + "iopub.status.idle": "2023-08-22T07:00:52.221771Z", + "shell.execute_reply": "2023-08-22T07:00:52.221413Z" }, "lines_to_next_cell": 2 }, @@ -1467,10 +1467,10 @@ "id": "b6ebefa7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.283282Z", - "iopub.status.busy": "2023-08-21T02:30:06.283123Z", - "iopub.status.idle": "2023-08-21T02:30:06.327003Z", - "shell.execute_reply": "2023-08-21T02:30:06.326646Z" + "iopub.execute_input": "2023-08-22T07:00:52.223735Z", + "iopub.status.busy": "2023-08-22T07:00:52.223584Z", + "iopub.status.idle": "2023-08-22T07:00:52.263558Z", + "shell.execute_reply": "2023-08-22T07:00:52.263236Z" } }, "outputs": [ @@ -1611,10 +1611,10 @@ "id": "098f42ea", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.329058Z", - "iopub.status.busy": "2023-08-21T02:30:06.328927Z", - "iopub.status.idle": "2023-08-21T02:30:06.332782Z", - "shell.execute_reply": "2023-08-21T02:30:06.332425Z" + "iopub.execute_input": "2023-08-22T07:00:52.265430Z", + "iopub.status.busy": "2023-08-22T07:00:52.265309Z", + "iopub.status.idle": "2023-08-22T07:00:52.269361Z", + "shell.execute_reply": "2023-08-22T07:00:52.268968Z" } }, "outputs": [], @@ -1649,10 +1649,10 @@ "id": "26d5d0d0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.334692Z", - "iopub.status.busy": "2023-08-21T02:30:06.334589Z", - "iopub.status.idle": "2023-08-21T02:30:06.344047Z", - "shell.execute_reply": "2023-08-21T02:30:06.343708Z" + "iopub.execute_input": "2023-08-22T07:00:52.271325Z", + "iopub.status.busy": "2023-08-22T07:00:52.271227Z", + "iopub.status.idle": "2023-08-22T07:00:52.280726Z", + "shell.execute_reply": "2023-08-22T07:00:52.280283Z" } }, "outputs": [], @@ -1681,10 +1681,10 @@ "id": "77500663", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.345660Z", - "iopub.status.busy": "2023-08-21T02:30:06.345575Z", - "iopub.status.idle": "2023-08-21T02:30:06.350086Z", - "shell.execute_reply": "2023-08-21T02:30:06.349797Z" + "iopub.execute_input": "2023-08-22T07:00:52.282793Z", + "iopub.status.busy": "2023-08-22T07:00:52.282661Z", + "iopub.status.idle": "2023-08-22T07:00:52.287078Z", + "shell.execute_reply": "2023-08-22T07:00:52.286749Z" } }, "outputs": [ @@ -1793,10 +1793,10 @@ "id": "74324a56", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.351738Z", - "iopub.status.busy": "2023-08-21T02:30:06.351549Z", - "iopub.status.idle": "2023-08-21T02:30:06.444268Z", - "shell.execute_reply": "2023-08-21T02:30:06.441484Z" + "iopub.execute_input": "2023-08-22T07:00:52.289254Z", + "iopub.status.busy": "2023-08-22T07:00:52.289116Z", + "iopub.status.idle": "2023-08-22T07:00:52.327077Z", + "shell.execute_reply": "2023-08-22T07:00:52.313280Z" } }, "outputs": [], @@ -1840,10 +1840,10 @@ "id": "d4be10c2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.449822Z", - "iopub.status.busy": "2023-08-21T02:30:06.449515Z", - "iopub.status.idle": "2023-08-21T02:30:06.458388Z", - "shell.execute_reply": "2023-08-21T02:30:06.457673Z" + "iopub.execute_input": "2023-08-22T07:00:52.360673Z", + "iopub.status.busy": "2023-08-22T07:00:52.360466Z", + "iopub.status.idle": "2023-08-22T07:00:52.386485Z", + "shell.execute_reply": "2023-08-22T07:00:52.383597Z" }, "lines_to_next_cell": 0 }, @@ -1871,10 +1871,10 @@ "id": "c98d396f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.461931Z", - "iopub.status.busy": "2023-08-21T02:30:06.461787Z", - "iopub.status.idle": "2023-08-21T02:30:06.624349Z", - "shell.execute_reply": "2023-08-21T02:30:06.624026Z" + "iopub.execute_input": "2023-08-22T07:00:52.411166Z", + "iopub.status.busy": "2023-08-22T07:00:52.410817Z", + "iopub.status.idle": "2023-08-22T07:00:52.535664Z", + "shell.execute_reply": "2023-08-22T07:00:52.534128Z" } }, "outputs": [], @@ -1900,10 +1900,10 @@ "id": "caf627bc", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.626165Z", - "iopub.status.busy": "2023-08-21T02:30:06.626054Z", - "iopub.status.idle": "2023-08-21T02:30:06.630808Z", - "shell.execute_reply": "2023-08-21T02:30:06.630542Z" + "iopub.execute_input": "2023-08-22T07:00:52.538055Z", + "iopub.status.busy": "2023-08-22T07:00:52.537880Z", + "iopub.status.idle": "2023-08-22T07:00:52.547315Z", + "shell.execute_reply": "2023-08-22T07:00:52.546097Z" }, "lines_to_next_cell": 2 }, @@ -2008,10 +2008,10 @@ "id": "e63242f9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.632357Z", - "iopub.status.busy": "2023-08-21T02:30:06.632261Z", - "iopub.status.idle": "2023-08-21T02:30:06.634630Z", - "shell.execute_reply": "2023-08-21T02:30:06.634305Z" + "iopub.execute_input": "2023-08-22T07:00:52.553567Z", + "iopub.status.busy": "2023-08-22T07:00:52.553399Z", + "iopub.status.idle": "2023-08-22T07:00:52.556617Z", + "shell.execute_reply": "2023-08-22T07:00:52.556076Z" } }, "outputs": [ @@ -2044,10 +2044,10 @@ "id": "338db71d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.636188Z", - "iopub.status.busy": "2023-08-21T02:30:06.636081Z", - "iopub.status.idle": "2023-08-21T02:30:06.791856Z", - "shell.execute_reply": "2023-08-21T02:30:06.791521Z" + "iopub.execute_input": "2023-08-22T07:00:52.558893Z", + "iopub.status.busy": "2023-08-22T07:00:52.558778Z", + "iopub.status.idle": "2023-08-22T07:00:52.706054Z", + "shell.execute_reply": "2023-08-22T07:00:52.705654Z" } }, "outputs": [ @@ -2096,10 +2096,10 @@ "id": "c1db6e15", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.793629Z", - "iopub.status.busy": "2023-08-21T02:30:06.793538Z", - "iopub.status.idle": "2023-08-21T02:30:06.992155Z", - "shell.execute_reply": "2023-08-21T02:30:06.991803Z" + "iopub.execute_input": "2023-08-22T07:00:52.707873Z", + "iopub.status.busy": "2023-08-22T07:00:52.707772Z", + "iopub.status.idle": "2023-08-22T07:00:52.850431Z", + "shell.execute_reply": "2023-08-22T07:00:52.849948Z" } }, "outputs": [ @@ -2152,10 +2152,10 @@ "id": "02ea4212", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:06.993929Z", - "iopub.status.busy": "2023-08-21T02:30:06.993819Z", - "iopub.status.idle": "2023-08-21T02:30:07.011557Z", - "shell.execute_reply": "2023-08-21T02:30:07.011276Z" + "iopub.execute_input": "2023-08-22T07:00:52.852333Z", + "iopub.status.busy": "2023-08-22T07:00:52.852184Z", + "iopub.status.idle": "2023-08-22T07:00:52.869743Z", + "shell.execute_reply": "2023-08-22T07:00:52.869438Z" }, "lines_to_next_cell": 2 }, @@ -2259,10 +2259,10 @@ "id": "0ac610d5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:07.013331Z", - "iopub.status.busy": "2023-08-21T02:30:07.013187Z", - "iopub.status.idle": "2023-08-21T02:30:07.030401Z", - "shell.execute_reply": "2023-08-21T02:30:07.030073Z" + "iopub.execute_input": "2023-08-22T07:00:52.871905Z", + "iopub.status.busy": "2023-08-22T07:00:52.871758Z", + "iopub.status.idle": "2023-08-22T07:00:52.889214Z", + "shell.execute_reply": "2023-08-22T07:00:52.888915Z" }, "lines_to_next_cell": 2 }, @@ -2369,10 +2369,10 @@ "id": "107cedad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:07.032008Z", - "iopub.status.busy": "2023-08-21T02:30:07.031887Z", - "iopub.status.idle": "2023-08-21T02:30:07.160931Z", - "shell.execute_reply": "2023-08-21T02:30:07.160639Z" + "iopub.execute_input": "2023-08-22T07:00:52.891264Z", + "iopub.status.busy": "2023-08-22T07:00:52.891105Z", + "iopub.status.idle": "2023-08-22T07:00:52.999600Z", + "shell.execute_reply": "2023-08-22T07:00:52.999278Z" }, "lines_to_next_cell": 2 }, @@ -2474,10 +2474,10 @@ "id": "334eb331", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:07.162793Z", - "iopub.status.busy": "2023-08-21T02:30:07.162651Z", - "iopub.status.idle": "2023-08-21T02:30:07.291875Z", - "shell.execute_reply": "2023-08-21T02:30:07.291550Z" + "iopub.execute_input": "2023-08-22T07:00:53.001288Z", + "iopub.status.busy": "2023-08-22T07:00:53.001160Z", + "iopub.status.idle": "2023-08-22T07:00:53.112657Z", + "shell.execute_reply": "2023-08-22T07:00:53.112061Z" }, "lines_to_next_cell": 2 }, @@ -2582,10 +2582,10 @@ "id": "421811c5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:07.293545Z", - "iopub.status.busy": "2023-08-21T02:30:07.293433Z", - "iopub.status.idle": "2023-08-21T02:30:07.532213Z", - "shell.execute_reply": "2023-08-21T02:30:07.531293Z" + "iopub.execute_input": "2023-08-22T07:00:53.115234Z", + "iopub.status.busy": "2023-08-22T07:00:53.114902Z", + "iopub.status.idle": "2023-08-22T07:00:53.349069Z", + "shell.execute_reply": "2023-08-22T07:00:53.326280Z" }, "lines_to_next_cell": 2 }, @@ -2703,7 +2703,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -2716,7 +2716,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch12-unsup-lab.Rmd b/Ch12-unsup-lab.Rmd index 8f885f4..164607a 100644 --- a/Ch12-unsup-lab.Rmd +++ b/Ch12-unsup-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch12-unsup-lab.ipynb b/Ch12-unsup-lab.ipynb index 54b3379..d9a9a1e 100644 --- a/Ch12-unsup-lab.ipynb +++ b/Ch12-unsup-lab.ipynb @@ -22,10 +22,10 @@ "id": "6d5ba583", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:08.846762Z", - "iopub.status.busy": "2023-08-21T02:30:08.846653Z", - "iopub.status.idle": "2023-08-21T02:30:09.939364Z", - "shell.execute_reply": "2023-08-21T02:30:09.939004Z" + "iopub.execute_input": "2023-08-22T07:00:56.821728Z", + "iopub.status.busy": "2023-08-22T07:00:56.821428Z", + "iopub.status.idle": "2023-08-22T07:00:57.569366Z", + "shell.execute_reply": "2023-08-22T07:00:57.569068Z" }, "lines_to_next_cell": 0 }, @@ -55,10 +55,10 @@ "id": "64c83257", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:09.941473Z", - "iopub.status.busy": "2023-08-21T02:30:09.941296Z", - "iopub.status.idle": "2023-08-21T02:30:10.036632Z", - "shell.execute_reply": "2023-08-21T02:30:10.036163Z" + "iopub.execute_input": "2023-08-22T07:00:57.571332Z", + "iopub.status.busy": "2023-08-22T07:00:57.571181Z", + "iopub.status.idle": "2023-08-22T07:00:59.834786Z", + "shell.execute_reply": "2023-08-22T07:00:59.834273Z" } }, "outputs": [], @@ -92,10 +92,10 @@ "id": "04ec4481", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:10.038974Z", - "iopub.status.busy": "2023-08-21T02:30:10.038734Z", - "iopub.status.idle": "2023-08-21T02:30:11.214222Z", - "shell.execute_reply": "2023-08-21T02:30:11.213910Z" + "iopub.execute_input": "2023-08-22T07:00:59.837457Z", + "iopub.status.busy": "2023-08-22T07:00:59.837321Z", + "iopub.status.idle": "2023-08-22T07:01:01.128712Z", + "shell.execute_reply": "2023-08-22T07:01:01.128206Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "id": "1b66036a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.216028Z", - "iopub.status.busy": "2023-08-21T02:30:11.215902Z", - "iopub.status.idle": "2023-08-21T02:30:11.218553Z", - "shell.execute_reply": "2023-08-21T02:30:11.218232Z" + "iopub.execute_input": "2023-08-22T07:01:01.131204Z", + "iopub.status.busy": "2023-08-22T07:01:01.130984Z", + "iopub.status.idle": "2023-08-22T07:01:01.135700Z", + "shell.execute_reply": "2023-08-22T07:01:01.134242Z" } }, "outputs": [ @@ -595,10 +595,10 @@ "id": "52e900fd", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.220090Z", - "iopub.status.busy": "2023-08-21T02:30:11.219971Z", - "iopub.status.idle": "2023-08-21T02:30:11.223332Z", - "shell.execute_reply": "2023-08-21T02:30:11.223004Z" + "iopub.execute_input": "2023-08-22T07:01:01.141963Z", + "iopub.status.busy": "2023-08-22T07:01:01.141493Z", + "iopub.status.idle": "2023-08-22T07:01:01.148106Z", + "shell.execute_reply": "2023-08-22T07:01:01.147604Z" }, "lines_to_next_cell": 2 }, @@ -638,10 +638,10 @@ "id": "68684f78", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.225020Z", - "iopub.status.busy": "2023-08-21T02:30:11.224913Z", - "iopub.status.idle": "2023-08-21T02:30:11.228160Z", - "shell.execute_reply": "2023-08-21T02:30:11.227843Z" + "iopub.execute_input": "2023-08-22T07:01:01.156074Z", + "iopub.status.busy": "2023-08-22T07:01:01.154480Z", + "iopub.status.idle": "2023-08-22T07:01:01.168063Z", + "shell.execute_reply": "2023-08-22T07:01:01.167370Z" } }, "outputs": [ @@ -692,10 +692,10 @@ "id": "d2b7caf9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.229847Z", - "iopub.status.busy": "2023-08-21T02:30:11.229717Z", - "iopub.status.idle": "2023-08-21T02:30:11.233108Z", - "shell.execute_reply": "2023-08-21T02:30:11.232828Z" + "iopub.execute_input": "2023-08-22T07:01:01.171694Z", + "iopub.status.busy": "2023-08-22T07:01:01.171419Z", + "iopub.status.idle": "2023-08-22T07:01:01.175704Z", + "shell.execute_reply": "2023-08-22T07:01:01.174949Z" }, "lines_to_next_cell": 0 }, @@ -722,10 +722,10 @@ "id": "de8f57fa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.234711Z", - "iopub.status.busy": "2023-08-21T02:30:11.234595Z", - "iopub.status.idle": "2023-08-21T02:30:11.241074Z", - "shell.execute_reply": "2023-08-21T02:30:11.239548Z" + "iopub.execute_input": "2023-08-22T07:01:01.179112Z", + "iopub.status.busy": "2023-08-22T07:01:01.178950Z", + "iopub.status.idle": "2023-08-22T07:01:01.181409Z", + "shell.execute_reply": "2023-08-22T07:01:01.180781Z" }, "lines_to_next_cell": 0 }, @@ -751,10 +751,10 @@ "id": "26c45f1e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.242595Z", - "iopub.status.busy": "2023-08-21T02:30:11.242489Z", - "iopub.status.idle": "2023-08-21T02:30:11.246323Z", - "shell.execute_reply": "2023-08-21T02:30:11.246064Z" + "iopub.execute_input": "2023-08-22T07:01:01.184888Z", + "iopub.status.busy": "2023-08-22T07:01:01.184710Z", + "iopub.status.idle": "2023-08-22T07:01:01.188530Z", + "shell.execute_reply": "2023-08-22T07:01:01.188252Z" } }, "outputs": [ @@ -792,10 +792,10 @@ "id": "3097e99d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.247882Z", - "iopub.status.busy": "2023-08-21T02:30:11.247777Z", - "iopub.status.idle": "2023-08-21T02:30:11.250076Z", - "shell.execute_reply": "2023-08-21T02:30:11.249792Z" + "iopub.execute_input": "2023-08-22T07:01:01.190309Z", + "iopub.status.busy": "2023-08-22T07:01:01.190146Z", + "iopub.status.idle": "2023-08-22T07:01:01.193028Z", + "shell.execute_reply": "2023-08-22T07:01:01.192570Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "id": "c071a242", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.251690Z", - "iopub.status.busy": "2023-08-21T02:30:11.251589Z", - "iopub.status.idle": "2023-08-21T02:30:11.253710Z", - "shell.execute_reply": "2023-08-21T02:30:11.253213Z" + "iopub.execute_input": "2023-08-22T07:01:01.196049Z", + "iopub.status.busy": "2023-08-22T07:01:01.195747Z", + "iopub.status.idle": "2023-08-22T07:01:01.198277Z", + "shell.execute_reply": "2023-08-22T07:01:01.197846Z" }, "lines_to_next_cell": 0 }, @@ -858,10 +858,10 @@ "id": "c9bcab06", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.255475Z", - "iopub.status.busy": "2023-08-21T02:30:11.255331Z", - "iopub.status.idle": "2023-08-21T02:30:11.257666Z", - "shell.execute_reply": "2023-08-21T02:30:11.257404Z" + "iopub.execute_input": "2023-08-22T07:01:01.200305Z", + "iopub.status.busy": "2023-08-22T07:01:01.200177Z", + "iopub.status.idle": "2023-08-22T07:01:01.202888Z", + "shell.execute_reply": "2023-08-22T07:01:01.202483Z" } }, "outputs": [ @@ -901,10 +901,10 @@ "id": "7375ab13", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.259208Z", - "iopub.status.busy": "2023-08-21T02:30:11.259106Z", - "iopub.status.idle": "2023-08-21T02:30:11.411821Z", - "shell.execute_reply": "2023-08-21T02:30:11.411424Z" + "iopub.execute_input": "2023-08-22T07:01:01.204941Z", + "iopub.status.busy": "2023-08-22T07:01:01.204821Z", + "iopub.status.idle": "2023-08-22T07:01:01.305551Z", + "shell.execute_reply": "2023-08-22T07:01:01.304502Z" }, "lines_to_next_cell": 0 }, @@ -951,10 +951,10 @@ "id": "4c1988de", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.413868Z", - "iopub.status.busy": "2023-08-21T02:30:11.413708Z", - "iopub.status.idle": "2023-08-21T02:30:11.510731Z", - "shell.execute_reply": "2023-08-21T02:30:11.510392Z" + "iopub.execute_input": "2023-08-22T07:01:01.308684Z", + "iopub.status.busy": "2023-08-22T07:01:01.308547Z", + "iopub.status.idle": "2023-08-22T07:01:01.398261Z", + "shell.execute_reply": "2023-08-22T07:01:01.397687Z" } }, "outputs": [ @@ -998,10 +998,10 @@ "id": "965c6320", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.512534Z", - "iopub.status.busy": "2023-08-21T02:30:11.512382Z", - "iopub.status.idle": "2023-08-21T02:30:11.515359Z", - "shell.execute_reply": "2023-08-21T02:30:11.514710Z" + "iopub.execute_input": "2023-08-22T07:01:01.400814Z", + "iopub.status.busy": "2023-08-22T07:01:01.400674Z", + "iopub.status.idle": "2023-08-22T07:01:01.404022Z", + "shell.execute_reply": "2023-08-22T07:01:01.403489Z" }, "lines_to_next_cell": 2 }, @@ -1036,10 +1036,10 @@ "id": "cd5e1663", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.517105Z", - "iopub.status.busy": "2023-08-21T02:30:11.516982Z", - "iopub.status.idle": "2023-08-21T02:30:11.519375Z", - "shell.execute_reply": "2023-08-21T02:30:11.519076Z" + "iopub.execute_input": "2023-08-22T07:01:01.405922Z", + "iopub.status.busy": "2023-08-22T07:01:01.405818Z", + "iopub.status.idle": "2023-08-22T07:01:01.409000Z", + "shell.execute_reply": "2023-08-22T07:01:01.408413Z" }, "lines_to_next_cell": 0 }, @@ -1074,10 +1074,10 @@ "id": "e711d1be", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.520951Z", - "iopub.status.busy": "2023-08-21T02:30:11.520847Z", - "iopub.status.idle": "2023-08-21T02:30:11.523283Z", - "shell.execute_reply": "2023-08-21T02:30:11.523005Z" + "iopub.execute_input": "2023-08-22T07:01:01.410782Z", + "iopub.status.busy": "2023-08-22T07:01:01.410676Z", + "iopub.status.idle": "2023-08-22T07:01:01.413542Z", + "shell.execute_reply": "2023-08-22T07:01:01.413036Z" }, "lines_to_next_cell": 0 }, @@ -1115,10 +1115,10 @@ "id": "e122eb41", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.524835Z", - "iopub.status.busy": "2023-08-21T02:30:11.524733Z", - "iopub.status.idle": "2023-08-21T02:30:11.658787Z", - "shell.execute_reply": "2023-08-21T02:30:11.658413Z" + "iopub.execute_input": "2023-08-22T07:01:01.415587Z", + "iopub.status.busy": "2023-08-22T07:01:01.415405Z", + "iopub.status.idle": "2023-08-22T07:01:01.537691Z", + "shell.execute_reply": "2023-08-22T07:01:01.537297Z" }, "lines_to_next_cell": 0 }, @@ -1151,10 +1151,10 @@ "id": "bef47d90", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.661199Z", - "iopub.status.busy": "2023-08-21T02:30:11.661017Z", - "iopub.status.idle": "2023-08-21T02:30:11.782427Z", - "shell.execute_reply": "2023-08-21T02:30:11.781635Z" + "iopub.execute_input": "2023-08-22T07:01:01.540279Z", + "iopub.status.busy": "2023-08-22T07:01:01.539986Z", + "iopub.status.idle": "2023-08-22T07:01:01.632911Z", + "shell.execute_reply": "2023-08-22T07:01:01.632328Z" }, "lines_to_next_cell": 0 }, @@ -1199,10 +1199,10 @@ "id": "f3300d9e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.785513Z", - "iopub.status.busy": "2023-08-21T02:30:11.785329Z", - "iopub.status.idle": "2023-08-21T02:30:11.789028Z", - "shell.execute_reply": "2023-08-21T02:30:11.788419Z" + "iopub.execute_input": "2023-08-22T07:01:01.635302Z", + "iopub.status.busy": "2023-08-22T07:01:01.634975Z", + "iopub.status.idle": "2023-08-22T07:01:01.638324Z", + "shell.execute_reply": "2023-08-22T07:01:01.637831Z" }, "lines_to_next_cell": 0 }, @@ -1248,10 +1248,10 @@ "id": "20e6009f", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.791569Z", - "iopub.status.busy": "2023-08-21T02:30:11.791444Z", - "iopub.status.idle": "2023-08-21T02:30:11.795478Z", - "shell.execute_reply": "2023-08-21T02:30:11.794972Z" + "iopub.execute_input": "2023-08-22T07:01:01.640494Z", + "iopub.status.busy": "2023-08-22T07:01:01.640389Z", + "iopub.status.idle": "2023-08-22T07:01:01.643841Z", + "shell.execute_reply": "2023-08-22T07:01:01.643438Z" }, "lines_to_next_cell": 0 }, @@ -1289,10 +1289,10 @@ "id": "7d9937cf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.797640Z", - "iopub.status.busy": "2023-08-21T02:30:11.797509Z", - "iopub.status.idle": "2023-08-21T02:30:11.800846Z", - "shell.execute_reply": "2023-08-21T02:30:11.800294Z" + "iopub.execute_input": "2023-08-22T07:01:01.646567Z", + "iopub.status.busy": "2023-08-22T07:01:01.646396Z", + "iopub.status.idle": "2023-08-22T07:01:01.649238Z", + "shell.execute_reply": "2023-08-22T07:01:01.648799Z" } }, "outputs": [ @@ -1320,10 +1320,10 @@ "id": "e58f83a3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.808398Z", - "iopub.status.busy": "2023-08-21T02:30:11.808136Z", - "iopub.status.idle": "2023-08-21T02:30:11.811679Z", - "shell.execute_reply": "2023-08-21T02:30:11.811074Z" + "iopub.execute_input": "2023-08-22T07:01:01.651066Z", + "iopub.status.busy": "2023-08-22T07:01:01.650908Z", + "iopub.status.idle": "2023-08-22T07:01:01.653787Z", + "shell.execute_reply": "2023-08-22T07:01:01.653263Z" }, "lines_to_next_cell": 0 }, @@ -1360,10 +1360,10 @@ "id": "5c4f9b34", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.813485Z", - "iopub.status.busy": "2023-08-21T02:30:11.813382Z", - "iopub.status.idle": "2023-08-21T02:30:11.816392Z", - "shell.execute_reply": "2023-08-21T02:30:11.815967Z" + "iopub.execute_input": "2023-08-22T07:01:01.656687Z", + "iopub.status.busy": "2023-08-22T07:01:01.656390Z", + "iopub.status.idle": "2023-08-22T07:01:01.659789Z", + "shell.execute_reply": "2023-08-22T07:01:01.659269Z" } }, "outputs": [ @@ -1390,10 +1390,10 @@ "id": "0ce84f1b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.818183Z", - "iopub.status.busy": "2023-08-21T02:30:11.818037Z", - "iopub.status.idle": "2023-08-21T02:30:11.820586Z", - "shell.execute_reply": "2023-08-21T02:30:11.820295Z" + "iopub.execute_input": "2023-08-22T07:01:01.661783Z", + "iopub.status.busy": "2023-08-22T07:01:01.661671Z", + "iopub.status.idle": "2023-08-22T07:01:01.664431Z", + "shell.execute_reply": "2023-08-22T07:01:01.664078Z" }, "lines_to_next_cell": 0 }, @@ -1435,10 +1435,10 @@ "id": "cd8b4bed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.822223Z", - "iopub.status.busy": "2023-08-21T02:30:11.822124Z", - "iopub.status.idle": "2023-08-21T02:30:11.824710Z", - "shell.execute_reply": "2023-08-21T02:30:11.824451Z" + "iopub.execute_input": "2023-08-22T07:01:01.666675Z", + "iopub.status.busy": "2023-08-22T07:01:01.666540Z", + "iopub.status.idle": "2023-08-22T07:01:01.669352Z", + "shell.execute_reply": "2023-08-22T07:01:01.668883Z" } }, "outputs": [], @@ -1475,10 +1475,10 @@ "id": "7f3bc8f9", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.826294Z", - "iopub.status.busy": "2023-08-21T02:30:11.826188Z", - "iopub.status.idle": "2023-08-21T02:30:11.828326Z", - "shell.execute_reply": "2023-08-21T02:30:11.827946Z" + "iopub.execute_input": "2023-08-22T07:01:01.671494Z", + "iopub.status.busy": "2023-08-22T07:01:01.671325Z", + "iopub.status.idle": "2023-08-22T07:01:01.673688Z", + "shell.execute_reply": "2023-08-22T07:01:01.673299Z" }, "lines_to_next_cell": 0 }, @@ -1508,10 +1508,10 @@ "id": "771a46a7", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.830112Z", - "iopub.status.busy": "2023-08-21T02:30:11.829990Z", - "iopub.status.idle": "2023-08-21T02:30:11.832019Z", - "shell.execute_reply": "2023-08-21T02:30:11.831653Z" + "iopub.execute_input": "2023-08-22T07:01:01.675631Z", + "iopub.status.busy": "2023-08-22T07:01:01.675424Z", + "iopub.status.idle": "2023-08-22T07:01:01.678059Z", + "shell.execute_reply": "2023-08-22T07:01:01.677393Z" } }, "outputs": [], @@ -1536,10 +1536,10 @@ "id": "1416f048", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.834018Z", - "iopub.status.busy": "2023-08-21T02:30:11.833888Z", - "iopub.status.idle": "2023-08-21T02:30:11.836065Z", - "shell.execute_reply": "2023-08-21T02:30:11.835740Z" + "iopub.execute_input": "2023-08-22T07:01:01.680424Z", + "iopub.status.busy": "2023-08-22T07:01:01.680179Z", + "iopub.status.idle": "2023-08-22T07:01:01.682713Z", + "shell.execute_reply": "2023-08-22T07:01:01.682431Z" }, "lines_to_next_cell": 0 }, @@ -1576,10 +1576,10 @@ "id": "9eff34aa", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.838230Z", - "iopub.status.busy": "2023-08-21T02:30:11.838081Z", - "iopub.status.idle": "2023-08-21T02:30:11.841552Z", - "shell.execute_reply": "2023-08-21T02:30:11.841240Z" + "iopub.execute_input": "2023-08-22T07:01:01.684370Z", + "iopub.status.busy": "2023-08-22T07:01:01.684237Z", + "iopub.status.idle": "2023-08-22T07:01:01.688888Z", + "shell.execute_reply": "2023-08-22T07:01:01.688576Z" } }, "outputs": [ @@ -1631,10 +1631,10 @@ "id": "7815b948", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.843245Z", - "iopub.status.busy": "2023-08-21T02:30:11.843118Z", - "iopub.status.idle": "2023-08-21T02:30:11.846592Z", - "shell.execute_reply": "2023-08-21T02:30:11.846200Z" + "iopub.execute_input": "2023-08-22T07:01:01.690940Z", + "iopub.status.busy": "2023-08-22T07:01:01.690758Z", + "iopub.status.idle": "2023-08-22T07:01:01.694473Z", + "shell.execute_reply": "2023-08-22T07:01:01.694113Z" }, "lines_to_next_cell": 2 }, @@ -1689,10 +1689,10 @@ "id": "f63cf4b8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.848430Z", - "iopub.status.busy": "2023-08-21T02:30:11.848311Z", - "iopub.status.idle": "2023-08-21T02:30:11.850755Z", - "shell.execute_reply": "2023-08-21T02:30:11.850430Z" + "iopub.execute_input": "2023-08-22T07:01:01.697098Z", + "iopub.status.busy": "2023-08-22T07:01:01.696989Z", + "iopub.status.idle": "2023-08-22T07:01:01.699944Z", + "shell.execute_reply": "2023-08-22T07:01:01.699302Z" }, "lines_to_next_cell": 0 }, @@ -1718,10 +1718,10 @@ "id": "f973c2d4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:11.852603Z", - "iopub.status.busy": "2023-08-21T02:30:11.852463Z", - "iopub.status.idle": "2023-08-21T02:30:12.419856Z", - "shell.execute_reply": "2023-08-21T02:30:12.416814Z" + "iopub.execute_input": "2023-08-22T07:01:01.701419Z", + "iopub.status.busy": "2023-08-22T07:01:01.701312Z", + "iopub.status.idle": "2023-08-22T07:01:02.037824Z", + "shell.execute_reply": "2023-08-22T07:01:02.037051Z" }, "lines_to_next_cell": 0 }, @@ -1746,10 +1746,10 @@ "id": "e980954b", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.436151Z", - "iopub.status.busy": "2023-08-21T02:30:12.434738Z", - "iopub.status.idle": "2023-08-21T02:30:12.484691Z", - "shell.execute_reply": "2023-08-21T02:30:12.459324Z" + "iopub.execute_input": "2023-08-22T07:01:02.040614Z", + "iopub.status.busy": "2023-08-22T07:01:02.040437Z", + "iopub.status.idle": "2023-08-22T07:01:02.044054Z", + "shell.execute_reply": "2023-08-22T07:01:02.043563Z" }, "lines_to_next_cell": 0 }, @@ -1788,10 +1788,10 @@ "id": "a94d452c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.510741Z", - "iopub.status.busy": "2023-08-21T02:30:12.510582Z", - "iopub.status.idle": "2023-08-21T02:30:12.636062Z", - "shell.execute_reply": "2023-08-21T02:30:12.635730Z" + "iopub.execute_input": "2023-08-22T07:01:02.046357Z", + "iopub.status.busy": "2023-08-22T07:01:02.046208Z", + "iopub.status.idle": "2023-08-22T07:01:02.149266Z", + "shell.execute_reply": "2023-08-22T07:01:02.148849Z" } }, "outputs": [ @@ -1835,10 +1835,10 @@ "id": "94ff654c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.638065Z", - "iopub.status.busy": "2023-08-21T02:30:12.637921Z", - "iopub.status.idle": "2023-08-21T02:30:12.806481Z", - "shell.execute_reply": "2023-08-21T02:30:12.805717Z" + "iopub.execute_input": "2023-08-22T07:01:02.151742Z", + "iopub.status.busy": "2023-08-22T07:01:02.151594Z", + "iopub.status.idle": "2023-08-22T07:01:02.271388Z", + "shell.execute_reply": "2023-08-22T07:01:02.270932Z" }, "lines_to_next_cell": 0 }, @@ -1884,10 +1884,10 @@ "id": "b3561317", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.808756Z", - "iopub.status.busy": "2023-08-21T02:30:12.808647Z", - "iopub.status.idle": "2023-08-21T02:30:12.868305Z", - "shell.execute_reply": "2023-08-21T02:30:12.867287Z" + "iopub.execute_input": "2023-08-22T07:01:02.274358Z", + "iopub.status.busy": "2023-08-22T07:01:02.273928Z", + "iopub.status.idle": "2023-08-22T07:01:02.290763Z", + "shell.execute_reply": "2023-08-22T07:01:02.290332Z" }, "lines_to_next_cell": 0 }, @@ -1957,10 +1957,10 @@ "id": "be9e4f9c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.871403Z", - "iopub.status.busy": "2023-08-21T02:30:12.871255Z", - "iopub.status.idle": "2023-08-21T02:30:12.877754Z", - "shell.execute_reply": "2023-08-21T02:30:12.877453Z" + "iopub.execute_input": "2023-08-22T07:01:02.293285Z", + "iopub.status.busy": "2023-08-22T07:01:02.293108Z", + "iopub.status.idle": "2023-08-22T07:01:02.297337Z", + "shell.execute_reply": "2023-08-22T07:01:02.296824Z" } }, "outputs": [ @@ -2004,10 +2004,10 @@ "id": "f80d8563", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.879743Z", - "iopub.status.busy": "2023-08-21T02:30:12.879627Z", - "iopub.status.idle": "2023-08-21T02:30:12.883455Z", - "shell.execute_reply": "2023-08-21T02:30:12.883063Z" + "iopub.execute_input": "2023-08-22T07:01:02.299662Z", + "iopub.status.busy": "2023-08-22T07:01:02.299457Z", + "iopub.status.idle": "2023-08-22T07:01:02.303357Z", + "shell.execute_reply": "2023-08-22T07:01:02.302779Z" } }, "outputs": [], @@ -2037,10 +2037,10 @@ "id": "83e7ccf8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.885091Z", - "iopub.status.busy": "2023-08-21T02:30:12.884985Z", - "iopub.status.idle": "2023-08-21T02:30:12.890499Z", - "shell.execute_reply": "2023-08-21T02:30:12.890138Z" + "iopub.execute_input": "2023-08-22T07:01:02.305759Z", + "iopub.status.busy": "2023-08-22T07:01:02.305577Z", + "iopub.status.idle": "2023-08-22T07:01:02.310695Z", + "shell.execute_reply": "2023-08-22T07:01:02.310382Z" } }, "outputs": [ @@ -2097,10 +2097,10 @@ "id": "56ee8cbf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:12.892483Z", - "iopub.status.busy": "2023-08-21T02:30:12.892271Z", - "iopub.status.idle": "2023-08-21T02:30:13.132407Z", - "shell.execute_reply": "2023-08-21T02:30:13.132103Z" + "iopub.execute_input": "2023-08-22T07:01:02.312692Z", + "iopub.status.busy": "2023-08-22T07:01:02.312323Z", + "iopub.status.idle": "2023-08-22T07:01:02.538970Z", + "shell.execute_reply": "2023-08-22T07:01:02.538582Z" } }, "outputs": [ @@ -2142,10 +2142,10 @@ "id": "10f4fc97", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.134084Z", - "iopub.status.busy": "2023-08-21T02:30:13.133962Z", - "iopub.status.idle": "2023-08-21T02:30:13.363909Z", - "shell.execute_reply": "2023-08-21T02:30:13.363594Z" + "iopub.execute_input": "2023-08-22T07:01:02.541376Z", + "iopub.status.busy": "2023-08-22T07:01:02.541208Z", + "iopub.status.idle": "2023-08-22T07:01:02.758968Z", + "shell.execute_reply": "2023-08-22T07:01:02.758508Z" } }, "outputs": [ @@ -2184,10 +2184,10 @@ "id": "3aed342a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.365591Z", - "iopub.status.busy": "2023-08-21T02:30:13.365487Z", - "iopub.status.idle": "2023-08-21T02:30:13.368978Z", - "shell.execute_reply": "2023-08-21T02:30:13.368698Z" + "iopub.execute_input": "2023-08-22T07:01:02.761325Z", + "iopub.status.busy": "2023-08-22T07:01:02.761152Z", + "iopub.status.idle": "2023-08-22T07:01:02.765136Z", + "shell.execute_reply": "2023-08-22T07:01:02.764674Z" } }, "outputs": [ @@ -2226,10 +2226,10 @@ "id": "49c6db0c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.370508Z", - "iopub.status.busy": "2023-08-21T02:30:13.370414Z", - "iopub.status.idle": "2023-08-21T02:30:13.373767Z", - "shell.execute_reply": "2023-08-21T02:30:13.373373Z" + "iopub.execute_input": "2023-08-22T07:01:02.767317Z", + "iopub.status.busy": "2023-08-22T07:01:02.767155Z", + "iopub.status.idle": "2023-08-22T07:01:02.771483Z", + "shell.execute_reply": "2023-08-22T07:01:02.771020Z" } }, "outputs": [ @@ -2312,10 +2312,10 @@ "id": "0ef4b7ec", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.375598Z", - "iopub.status.busy": "2023-08-21T02:30:13.375476Z", - "iopub.status.idle": "2023-08-21T02:30:13.613632Z", - "shell.execute_reply": "2023-08-21T02:30:13.613228Z" + "iopub.execute_input": "2023-08-22T07:01:02.773805Z", + "iopub.status.busy": "2023-08-22T07:01:02.773647Z", + "iopub.status.idle": "2023-08-22T07:01:03.002349Z", + "shell.execute_reply": "2023-08-22T07:01:03.001396Z" } }, "outputs": [ @@ -2367,10 +2367,10 @@ "id": "51761ef3", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.616266Z", - "iopub.status.busy": "2023-08-21T02:30:13.616114Z", - "iopub.status.idle": "2023-08-21T02:30:13.807244Z", - "shell.execute_reply": "2023-08-21T02:30:13.806904Z" + "iopub.execute_input": "2023-08-22T07:01:03.007714Z", + "iopub.status.busy": "2023-08-22T07:01:03.007387Z", + "iopub.status.idle": "2023-08-22T07:01:03.189542Z", + "shell.execute_reply": "2023-08-22T07:01:03.189234Z" }, "lines_to_next_cell": 2 }, @@ -2419,10 +2419,10 @@ "id": "3dbe7baf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.808881Z", - "iopub.status.busy": "2023-08-21T02:30:13.808757Z", - "iopub.status.idle": "2023-08-21T02:30:13.815572Z", - "shell.execute_reply": "2023-08-21T02:30:13.815234Z" + "iopub.execute_input": "2023-08-22T07:01:03.191496Z", + "iopub.status.busy": "2023-08-22T07:01:03.191325Z", + "iopub.status.idle": "2023-08-22T07:01:03.200433Z", + "shell.execute_reply": "2023-08-22T07:01:03.199967Z" } }, "outputs": [], @@ -2452,10 +2452,10 @@ "id": "8f4e9db0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.817455Z", - "iopub.status.busy": "2023-08-21T02:30:13.817327Z", - "iopub.status.idle": "2023-08-21T02:30:13.819934Z", - "shell.execute_reply": "2023-08-21T02:30:13.819588Z" + "iopub.execute_input": "2023-08-22T07:01:03.206306Z", + "iopub.status.busy": "2023-08-22T07:01:03.206039Z", + "iopub.status.idle": "2023-08-22T07:01:03.211349Z", + "shell.execute_reply": "2023-08-22T07:01:03.210526Z" }, "lines_to_next_cell": 2 }, @@ -2489,10 +2489,10 @@ "id": "6373db4d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.821622Z", - "iopub.status.busy": "2023-08-21T02:30:13.821498Z", - "iopub.status.idle": "2023-08-21T02:30:13.825550Z", - "shell.execute_reply": "2023-08-21T02:30:13.825258Z" + "iopub.execute_input": "2023-08-22T07:01:03.215280Z", + "iopub.status.busy": "2023-08-22T07:01:03.215107Z", + "iopub.status.idle": "2023-08-22T07:01:03.220745Z", + "shell.execute_reply": "2023-08-22T07:01:03.220268Z" }, "lines_to_next_cell": 2 }, @@ -2545,10 +2545,10 @@ "id": "9f185f83", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:13.827046Z", - "iopub.status.busy": "2023-08-21T02:30:13.826963Z", - "iopub.status.idle": "2023-08-21T02:30:15.128422Z", - "shell.execute_reply": "2023-08-21T02:30:15.127267Z" + "iopub.execute_input": "2023-08-22T07:01:03.224010Z", + "iopub.status.busy": "2023-08-22T07:01:03.223805Z", + "iopub.status.idle": "2023-08-22T07:01:03.756454Z", + "shell.execute_reply": "2023-08-22T07:01:03.755090Z" } }, "outputs": [], @@ -2577,10 +2577,10 @@ "id": "b044b197", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:15.132319Z", - "iopub.status.busy": "2023-08-21T02:30:15.130836Z", - "iopub.status.idle": "2023-08-21T02:30:15.450531Z", - "shell.execute_reply": "2023-08-21T02:30:15.450148Z" + "iopub.execute_input": "2023-08-22T07:01:03.760531Z", + "iopub.status.busy": "2023-08-22T07:01:03.759964Z", + "iopub.status.idle": "2023-08-22T07:01:04.106389Z", + "shell.execute_reply": "2023-08-22T07:01:04.105861Z" }, "lines_to_next_cell": 0 }, @@ -2647,10 +2647,10 @@ "id": "b2450bb2", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:15.453108Z", - "iopub.status.busy": "2023-08-21T02:30:15.452934Z", - "iopub.status.idle": "2023-08-21T02:30:15.641832Z", - "shell.execute_reply": "2023-08-21T02:30:15.641441Z" + "iopub.execute_input": "2023-08-22T07:01:04.108911Z", + "iopub.status.busy": "2023-08-22T07:01:04.108756Z", + "iopub.status.idle": "2023-08-22T07:01:04.295709Z", + "shell.execute_reply": "2023-08-22T07:01:04.295142Z" }, "lines_to_next_cell": 0 }, @@ -2720,10 +2720,10 @@ "id": "f3f85512", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:15.645761Z", - "iopub.status.busy": "2023-08-21T02:30:15.645577Z", - "iopub.status.idle": "2023-08-21T02:30:15.648982Z", - "shell.execute_reply": "2023-08-21T02:30:15.648549Z" + "iopub.execute_input": "2023-08-22T07:01:04.298506Z", + "iopub.status.busy": "2023-08-22T07:01:04.298369Z", + "iopub.status.idle": "2023-08-22T07:01:04.301034Z", + "shell.execute_reply": "2023-08-22T07:01:04.300583Z" } }, "outputs": [], @@ -2758,10 +2758,10 @@ "id": "5cbeeb19", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:15.651172Z", - "iopub.status.busy": "2023-08-21T02:30:15.651062Z", - "iopub.status.idle": "2023-08-21T02:30:17.138064Z", - "shell.execute_reply": "2023-08-21T02:30:17.137456Z" + "iopub.execute_input": "2023-08-22T07:01:04.303691Z", + "iopub.status.busy": "2023-08-22T07:01:04.303453Z", + "iopub.status.idle": "2023-08-22T07:01:05.507632Z", + "shell.execute_reply": "2023-08-22T07:01:05.507177Z" }, "lines_to_next_cell": 0 }, @@ -2811,10 +2811,10 @@ "id": "1eb3c92e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:17.141105Z", - "iopub.status.busy": "2023-08-21T02:30:17.140950Z", - "iopub.status.idle": "2023-08-21T02:30:17.153308Z", - "shell.execute_reply": "2023-08-21T02:30:17.152985Z" + "iopub.execute_input": "2023-08-22T07:01:05.509745Z", + "iopub.status.busy": "2023-08-22T07:01:05.509610Z", + "iopub.status.idle": "2023-08-22T07:01:05.520162Z", + "shell.execute_reply": "2023-08-22T07:01:05.519790Z" }, "lines_to_next_cell": 2 }, @@ -3005,10 +3005,10 @@ "id": "e3c2841c", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:17.155262Z", - "iopub.status.busy": "2023-08-21T02:30:17.155100Z", - "iopub.status.idle": "2023-08-21T02:30:17.595981Z", - "shell.execute_reply": "2023-08-21T02:30:17.595541Z" + "iopub.execute_input": "2023-08-22T07:01:05.522420Z", + "iopub.status.busy": "2023-08-22T07:01:05.522186Z", + "iopub.status.idle": "2023-08-22T07:01:05.914218Z", + "shell.execute_reply": "2023-08-22T07:01:05.913893Z" } }, "outputs": [ @@ -3054,10 +3054,10 @@ "id": "94dfe5a0", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:17.598183Z", - "iopub.status.busy": "2023-08-21T02:30:17.598049Z", - "iopub.status.idle": "2023-08-21T02:30:46.877046Z", - "shell.execute_reply": "2023-08-21T02:30:46.874804Z" + "iopub.execute_input": "2023-08-22T07:01:05.915828Z", + "iopub.status.busy": "2023-08-22T07:01:05.915706Z", + "iopub.status.idle": "2023-08-22T07:01:06.390699Z", + "shell.execute_reply": "2023-08-22T07:01:06.390367Z" } }, "outputs": [ @@ -3179,10 +3179,10 @@ "id": "abd51940", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:30:46.888673Z", - "iopub.status.busy": "2023-08-21T02:30:46.888240Z", - "iopub.status.idle": "2023-08-21T02:30:47.344737Z", - "shell.execute_reply": "2023-08-21T02:30:47.344286Z" + "iopub.execute_input": "2023-08-22T07:01:06.404283Z", + "iopub.status.busy": "2023-08-22T07:01:06.404155Z", + "iopub.status.idle": "2023-08-22T07:01:06.897582Z", + "shell.execute_reply": "2023-08-22T07:01:06.897221Z" }, "lines_to_next_cell": 0 }, @@ -3392,7 +3392,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -3405,7 +3405,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/Ch13-multiple-lab.Rmd b/Ch13-multiple-lab.Rmd index bee30c3..59d7b73 100644 --- a/Ch13-multiple-lab.Rmd +++ b/Ch13-multiple-lab.Rmd @@ -2,7 +2,7 @@ jupyter: jupytext: cell_metadata_filter: -all - formats: ipynb,Rmd + formats: Rmd,ipynb main_language: python text_representation: extension: .Rmd diff --git a/Ch13-multiple-lab.ipynb b/Ch13-multiple-lab.ipynb index bb9684d..0e8886d 100644 --- a/Ch13-multiple-lab.ipynb +++ b/Ch13-multiple-lab.ipynb @@ -26,10 +26,10 @@ "id": "7cc4fbeb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:16.417394Z", - "iopub.status.busy": "2023-08-21T02:29:16.417287Z", - "iopub.status.idle": "2023-08-21T02:29:17.613483Z", - "shell.execute_reply": "2023-08-21T02:29:17.613156Z" + "iopub.execute_input": "2023-08-22T07:01:10.471948Z", + "iopub.status.busy": "2023-08-22T07:01:10.471668Z", + "iopub.status.idle": "2023-08-22T07:01:11.350943Z", + "shell.execute_reply": "2023-08-22T07:01:11.350643Z" } }, "outputs": [], @@ -56,10 +56,10 @@ "id": "595efc18", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.615551Z", - "iopub.status.busy": "2023-08-21T02:29:17.615375Z", - "iopub.status.idle": "2023-08-21T02:29:17.617379Z", - "shell.execute_reply": "2023-08-21T02:29:17.617087Z" + "iopub.execute_input": "2023-08-22T07:01:11.352807Z", + "iopub.status.busy": "2023-08-22T07:01:11.352663Z", + "iopub.status.idle": "2023-08-22T07:01:11.354553Z", + "shell.execute_reply": "2023-08-22T07:01:11.354339Z" }, "lines_to_next_cell": 2 }, @@ -95,10 +95,10 @@ "id": "985d1d6e", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.618995Z", - "iopub.status.busy": "2023-08-21T02:29:17.618887Z", - "iopub.status.idle": "2023-08-21T02:29:17.620921Z", - "shell.execute_reply": "2023-08-21T02:29:17.620629Z" + "iopub.execute_input": "2023-08-22T07:01:11.355962Z", + "iopub.status.busy": "2023-08-22T07:01:11.355865Z", + "iopub.status.idle": "2023-08-22T07:01:11.357829Z", + "shell.execute_reply": "2023-08-22T07:01:11.357602Z" } }, "outputs": [], @@ -125,10 +125,10 @@ "id": "753d612a", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.622537Z", - "iopub.status.busy": "2023-08-21T02:29:17.622429Z", - "iopub.status.idle": "2023-08-21T02:29:17.626063Z", - "shell.execute_reply": "2023-08-21T02:29:17.625801Z" + "iopub.execute_input": "2023-08-22T07:01:11.359164Z", + "iopub.status.busy": "2023-08-22T07:01:11.359072Z", + "iopub.status.idle": "2023-08-22T07:01:11.362315Z", + "shell.execute_reply": "2023-08-22T07:01:11.362065Z" } }, "outputs": [ @@ -172,10 +172,10 @@ "id": "facd6569", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.627714Z", - "iopub.status.busy": "2023-08-21T02:29:17.627617Z", - "iopub.status.idle": "2023-08-21T02:29:17.651726Z", - "shell.execute_reply": "2023-08-21T02:29:17.651448Z" + "iopub.execute_input": "2023-08-22T07:01:11.363675Z", + "iopub.status.busy": "2023-08-22T07:01:11.363598Z", + "iopub.status.idle": "2023-08-22T07:01:11.382551Z", + "shell.execute_reply": "2023-08-22T07:01:11.382313Z" }, "lines_to_next_cell": 0 }, @@ -208,10 +208,10 @@ "id": "e89ef3eb", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.653344Z", - "iopub.status.busy": "2023-08-21T02:29:17.653256Z", - "iopub.status.idle": "2023-08-21T02:29:17.662644Z", - "shell.execute_reply": "2023-08-21T02:29:17.662346Z" + "iopub.execute_input": "2023-08-22T07:01:11.384012Z", + "iopub.status.busy": "2023-08-22T07:01:11.383929Z", + "iopub.status.idle": "2023-08-22T07:01:11.390854Z", + "shell.execute_reply": "2023-08-22T07:01:11.390622Z" }, "lines_to_next_cell": 0 }, @@ -309,10 +309,10 @@ "id": "ae184aaf", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.664327Z", - "iopub.status.busy": "2023-08-21T02:29:17.664213Z", - "iopub.status.idle": "2023-08-21T02:29:17.690928Z", - "shell.execute_reply": "2023-08-21T02:29:17.690657Z" + "iopub.execute_input": "2023-08-22T07:01:11.392278Z", + "iopub.status.busy": "2023-08-22T07:01:11.392193Z", + "iopub.status.idle": "2023-08-22T07:01:11.414541Z", + "shell.execute_reply": "2023-08-22T07:01:11.414284Z" }, "lines_to_next_cell": 0 }, @@ -421,10 +421,10 @@ "id": "0295fe68", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.692568Z", - "iopub.status.busy": "2023-08-21T02:29:17.692459Z", - "iopub.status.idle": "2023-08-21T02:29:17.899403Z", - "shell.execute_reply": "2023-08-21T02:29:17.899081Z" + "iopub.execute_input": "2023-08-22T07:01:11.416013Z", + "iopub.status.busy": "2023-08-22T07:01:11.415934Z", + "iopub.status.idle": "2023-08-22T07:01:11.601337Z", + "shell.execute_reply": "2023-08-22T07:01:11.601050Z" } }, "outputs": [ @@ -477,10 +477,10 @@ "id": "406e59a8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.901146Z", - "iopub.status.busy": "2023-08-21T02:29:17.901041Z", - "iopub.status.idle": "2023-08-21T02:29:17.939312Z", - "shell.execute_reply": "2023-08-21T02:29:17.939019Z" + "iopub.execute_input": "2023-08-22T07:01:11.603043Z", + "iopub.status.busy": "2023-08-22T07:01:11.602922Z", + "iopub.status.idle": "2023-08-22T07:01:11.637677Z", + "shell.execute_reply": "2023-08-22T07:01:11.637393Z" } }, "outputs": [ @@ -545,10 +545,10 @@ "id": "d4f6a247", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.941032Z", - "iopub.status.busy": "2023-08-21T02:29:17.940919Z", - "iopub.status.idle": "2023-08-21T02:29:17.943369Z", - "shell.execute_reply": "2023-08-21T02:29:17.943081Z" + "iopub.execute_input": "2023-08-22T07:01:11.639421Z", + "iopub.status.busy": "2023-08-22T07:01:11.639308Z", + "iopub.status.idle": "2023-08-22T07:01:11.641724Z", + "shell.execute_reply": "2023-08-22T07:01:11.641464Z" }, "lines_to_next_cell": 2 }, @@ -584,10 +584,10 @@ "id": "01a29d71", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.944859Z", - "iopub.status.busy": "2023-08-21T02:29:17.944760Z", - "iopub.status.idle": "2023-08-21T02:29:17.946888Z", - "shell.execute_reply": "2023-08-21T02:29:17.946639Z" + "iopub.execute_input": "2023-08-22T07:01:11.643173Z", + "iopub.status.busy": "2023-08-22T07:01:11.643074Z", + "iopub.status.idle": "2023-08-22T07:01:11.645284Z", + "shell.execute_reply": "2023-08-22T07:01:11.645035Z" } }, "outputs": [ @@ -626,10 +626,10 @@ "id": "95454eb4", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.948474Z", - "iopub.status.busy": "2023-08-21T02:29:17.948372Z", - "iopub.status.idle": "2023-08-21T02:29:17.990740Z", - "shell.execute_reply": "2023-08-21T02:29:17.990464Z" + "iopub.execute_input": "2023-08-22T07:01:11.646828Z", + "iopub.status.busy": "2023-08-22T07:01:11.646723Z", + "iopub.status.idle": "2023-08-22T07:01:11.681328Z", + "shell.execute_reply": "2023-08-22T07:01:11.681006Z" }, "lines_to_next_cell": 2 }, @@ -666,10 +666,10 @@ "id": "1f1ac764", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.992261Z", - "iopub.status.busy": "2023-08-21T02:29:17.992149Z", - "iopub.status.idle": "2023-08-21T02:29:17.995141Z", - "shell.execute_reply": "2023-08-21T02:29:17.994894Z" + "iopub.execute_input": "2023-08-22T07:01:11.683039Z", + "iopub.status.busy": "2023-08-22T07:01:11.682931Z", + "iopub.status.idle": "2023-08-22T07:01:11.685981Z", + "shell.execute_reply": "2023-08-22T07:01:11.685726Z" }, "lines_to_next_cell": 2 }, @@ -710,10 +710,10 @@ "id": "298d975d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:17.996686Z", - "iopub.status.busy": "2023-08-21T02:29:17.996590Z", - "iopub.status.idle": "2023-08-21T02:29:17.999332Z", - "shell.execute_reply": "2023-08-21T02:29:17.999076Z" + "iopub.execute_input": "2023-08-22T07:01:11.687516Z", + "iopub.status.busy": "2023-08-22T07:01:11.687436Z", + "iopub.status.idle": "2023-08-22T07:01:11.690137Z", + "shell.execute_reply": "2023-08-22T07:01:11.689879Z" } }, "outputs": [ @@ -762,10 +762,10 @@ "id": "be117713", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:18.000853Z", - "iopub.status.busy": "2023-08-21T02:29:18.000747Z", - "iopub.status.idle": "2023-08-21T02:29:18.487357Z", - "shell.execute_reply": "2023-08-21T02:29:18.487078Z" + "iopub.execute_input": "2023-08-22T07:01:11.691729Z", + "iopub.status.busy": "2023-08-22T07:01:11.691643Z", + "iopub.status.idle": "2023-08-22T07:01:12.083351Z", + "shell.execute_reply": "2023-08-22T07:01:12.083006Z" }, "lines_to_next_cell": 2 }, @@ -822,10 +822,10 @@ "id": "537c4ea8", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:18.489069Z", - "iopub.status.busy": "2023-08-21T02:29:18.488949Z", - "iopub.status.idle": "2023-08-21T02:29:18.570869Z", - "shell.execute_reply": "2023-08-21T02:29:18.570427Z" + "iopub.execute_input": "2023-08-22T07:01:12.085028Z", + "iopub.status.busy": "2023-08-22T07:01:12.084925Z", + "iopub.status.idle": "2023-08-22T07:01:12.158318Z", + "shell.execute_reply": "2023-08-22T07:01:12.158031Z" } }, "outputs": [ @@ -863,10 +863,10 @@ "id": "2c88ec87", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:18.572454Z", - "iopub.status.busy": "2023-08-21T02:29:18.572341Z", - "iopub.status.idle": "2023-08-21T02:29:19.005707Z", - "shell.execute_reply": "2023-08-21T02:29:19.005387Z" + "iopub.execute_input": "2023-08-22T07:01:12.160012Z", + "iopub.status.busy": "2023-08-22T07:01:12.159900Z", + "iopub.status.idle": "2023-08-22T07:01:12.510866Z", + "shell.execute_reply": "2023-08-22T07:01:12.510573Z" } }, "outputs": [], @@ -892,10 +892,10 @@ "id": "b6d56819", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.007847Z", - "iopub.status.busy": "2023-08-21T02:29:19.007564Z", - "iopub.status.idle": "2023-08-21T02:29:19.010742Z", - "shell.execute_reply": "2023-08-21T02:29:19.010371Z" + "iopub.execute_input": "2023-08-22T07:01:12.512518Z", + "iopub.status.busy": "2023-08-22T07:01:12.512432Z", + "iopub.status.idle": "2023-08-22T07:01:12.514982Z", + "shell.execute_reply": "2023-08-22T07:01:12.514769Z" } }, "outputs": [ @@ -937,10 +937,10 @@ "id": "b00da3a1", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.012400Z", - "iopub.status.busy": "2023-08-21T02:29:19.012298Z", - "iopub.status.idle": "2023-08-21T02:29:19.015314Z", - "shell.execute_reply": "2023-08-21T02:29:19.014978Z" + "iopub.execute_input": "2023-08-22T07:01:12.516361Z", + "iopub.status.busy": "2023-08-22T07:01:12.516283Z", + "iopub.status.idle": "2023-08-22T07:01:12.518438Z", + "shell.execute_reply": "2023-08-22T07:01:12.518191Z" }, "lines_to_next_cell": 0 }, @@ -981,10 +981,10 @@ "id": "1c230117", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.016857Z", - "iopub.status.busy": "2023-08-21T02:29:19.016769Z", - "iopub.status.idle": "2023-08-21T02:29:19.019332Z", - "shell.execute_reply": "2023-08-21T02:29:19.019032Z" + "iopub.execute_input": "2023-08-22T07:01:12.519863Z", + "iopub.status.busy": "2023-08-22T07:01:12.519780Z", + "iopub.status.idle": "2023-08-22T07:01:12.521828Z", + "shell.execute_reply": "2023-08-22T07:01:12.521610Z" }, "lines_to_next_cell": 2 }, @@ -1031,10 +1031,10 @@ "id": "62289650", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.021112Z", - "iopub.status.busy": "2023-08-21T02:29:19.020904Z", - "iopub.status.idle": "2023-08-21T02:29:19.023622Z", - "shell.execute_reply": "2023-08-21T02:29:19.023338Z" + "iopub.execute_input": "2023-08-22T07:01:12.523116Z", + "iopub.status.busy": "2023-08-22T07:01:12.523043Z", + "iopub.status.idle": "2023-08-22T07:01:12.525236Z", + "shell.execute_reply": "2023-08-22T07:01:12.525021Z" } }, "outputs": [], @@ -1065,10 +1065,10 @@ "id": "18b3c0ed", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.025191Z", - "iopub.status.busy": "2023-08-21T02:29:19.025074Z", - "iopub.status.idle": "2023-08-21T02:29:19.262207Z", - "shell.execute_reply": "2023-08-21T02:29:19.261823Z" + "iopub.execute_input": "2023-08-22T07:01:12.526613Z", + "iopub.status.busy": "2023-08-22T07:01:12.526539Z", + "iopub.status.idle": "2023-08-22T07:01:12.740275Z", + "shell.execute_reply": "2023-08-22T07:01:12.739986Z" }, "lines_to_next_cell": 2 }, @@ -1115,10 +1115,10 @@ "id": "eb79e606", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.264174Z", - "iopub.status.busy": "2023-08-21T02:29:19.264030Z", - "iopub.status.idle": "2023-08-21T02:29:19.339232Z", - "shell.execute_reply": "2023-08-21T02:29:19.338912Z" + "iopub.execute_input": "2023-08-22T07:01:12.742002Z", + "iopub.status.busy": "2023-08-22T07:01:12.741882Z", + "iopub.status.idle": "2023-08-22T07:01:12.844984Z", + "shell.execute_reply": "2023-08-22T07:01:12.844724Z" }, "lines_to_next_cell": 2 }, @@ -1166,10 +1166,10 @@ "id": "1afbcf47", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.341009Z", - "iopub.status.busy": "2023-08-21T02:29:19.340889Z", - "iopub.status.idle": "2023-08-21T02:29:19.344670Z", - "shell.execute_reply": "2023-08-21T02:29:19.344391Z" + "iopub.execute_input": "2023-08-22T07:01:12.846468Z", + "iopub.status.busy": "2023-08-22T07:01:12.846384Z", + "iopub.status.idle": "2023-08-22T07:01:12.849714Z", + "shell.execute_reply": "2023-08-22T07:01:12.849454Z" }, "lines_to_next_cell": 2 }, @@ -1219,10 +1219,10 @@ "id": "f73f4c6d", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:19.346368Z", - "iopub.status.busy": "2023-08-21T02:29:19.346227Z", - "iopub.status.idle": "2023-08-21T02:29:21.776569Z", - "shell.execute_reply": "2023-08-21T02:29:21.776267Z" + "iopub.execute_input": "2023-08-22T07:01:12.851134Z", + "iopub.status.busy": "2023-08-22T07:01:12.851053Z", + "iopub.status.idle": "2023-08-22T07:01:14.848052Z", + "shell.execute_reply": "2023-08-22T07:01:14.847788Z" }, "lines_to_next_cell": 2 }, @@ -1270,10 +1270,10 @@ "id": "062daf19", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:21.778366Z", - "iopub.status.busy": "2023-08-21T02:29:21.778242Z", - "iopub.status.idle": "2023-08-21T02:29:21.990476Z", - "shell.execute_reply": "2023-08-21T02:29:21.989965Z" + "iopub.execute_input": "2023-08-22T07:01:14.849560Z", + "iopub.status.busy": "2023-08-22T07:01:14.849477Z", + "iopub.status.idle": "2023-08-22T07:01:14.990286Z", + "shell.execute_reply": "2023-08-22T07:01:14.989980Z" }, "lines_to_next_cell": 0 }, @@ -1330,10 +1330,10 @@ "id": "6d14fcad", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:29:21.992665Z", - "iopub.status.busy": "2023-08-21T02:29:21.992515Z", - "iopub.status.idle": "2023-08-21T02:34:05.930300Z", - "shell.execute_reply": "2023-08-21T02:34:05.929181Z" + "iopub.execute_input": "2023-08-22T07:01:14.992440Z", + "iopub.status.busy": "2023-08-22T07:01:14.992326Z", + "iopub.status.idle": "2023-08-22T07:04:36.802040Z", + "shell.execute_reply": "2023-08-22T07:04:36.801740Z" } }, "outputs": [], @@ -1376,10 +1376,10 @@ "id": "8f0ec909", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:34:05.935513Z", - "iopub.status.busy": "2023-08-21T02:34:05.935323Z", - "iopub.status.idle": "2023-08-21T02:34:06.118079Z", - "shell.execute_reply": "2023-08-21T02:34:06.117633Z" + "iopub.execute_input": "2023-08-22T07:04:36.803805Z", + "iopub.status.busy": "2023-08-22T07:04:36.803712Z", + "iopub.status.idle": "2023-08-22T07:04:36.913862Z", + "shell.execute_reply": "2023-08-22T07:04:36.913579Z" } }, "outputs": [], @@ -1417,10 +1417,10 @@ "id": "f11339e5", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:34:06.120138Z", - "iopub.status.busy": "2023-08-21T02:34:06.119994Z", - "iopub.status.idle": "2023-08-21T02:34:06.123846Z", - "shell.execute_reply": "2023-08-21T02:34:06.123478Z" + "iopub.execute_input": "2023-08-22T07:04:36.915631Z", + "iopub.status.busy": "2023-08-22T07:04:36.915542Z", + "iopub.status.idle": "2023-08-22T07:04:36.918059Z", + "shell.execute_reply": "2023-08-22T07:04:36.917797Z" } }, "outputs": [ @@ -1472,10 +1472,10 @@ "id": "d2600773", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:34:06.126460Z", - "iopub.status.busy": "2023-08-21T02:34:06.126346Z", - "iopub.status.idle": "2023-08-21T02:34:06.129561Z", - "shell.execute_reply": "2023-08-21T02:34:06.129124Z" + "iopub.execute_input": "2023-08-22T07:04:36.919428Z", + "iopub.status.busy": "2023-08-22T07:04:36.919345Z", + "iopub.status.idle": "2023-08-22T07:04:36.921649Z", + "shell.execute_reply": "2023-08-22T07:04:36.921430Z" } }, "outputs": [ @@ -1540,10 +1540,10 @@ "id": "924b7705", "metadata": { "execution": { - "iopub.execute_input": "2023-08-21T02:34:06.131323Z", - "iopub.status.busy": "2023-08-21T02:34:06.131207Z", - "iopub.status.idle": "2023-08-21T02:34:06.216626Z", - "shell.execute_reply": "2023-08-21T02:34:06.216270Z" + "iopub.execute_input": "2023-08-22T07:04:36.923042Z", + "iopub.status.busy": "2023-08-22T07:04:36.922969Z", + "iopub.status.idle": "2023-08-22T07:04:36.986633Z", + "shell.execute_reply": "2023-08-22T07:04:36.986366Z" }, "lines_to_next_cell": 0 }, @@ -1578,7 +1578,7 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,Rmd", + "formats": "Rmd,ipynb", "main_language": "python" }, "language_info": { @@ -1591,7 +1591,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, From dc4fb733d78c19bedfa60176e74d9616acc5c7b4 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:17:08 -0700 Subject: [PATCH 15/18] stripped execution data --- Ch02-statlearn-lab.ipynb | 798 +++++----------------------------- Ch03-linreg-lab.ipynb | 252 ++--------- Ch04-classification-lab.ipynb | 553 ++++------------------- Ch05-resample-lab.ipynb | 182 ++------ Ch06-varselect-lab.ipynb | 399 +++-------------- Ch07-nonlin-lab.ipynb | 343 +++------------ Ch08-baggboost-lab.ipynb | 245 ++--------- Ch09-svm-lab.ipynb | 238 ++-------- Ch10-deeplearning-lab.ipynb | 791 +++++---------------------------- Ch11-surv-lab.ipynb | 245 ++--------- Ch12-unsup-lab.ipynb | 406 +++-------------- Ch13-multiple-lab.ipynb | 217 ++------- 12 files changed, 667 insertions(+), 4002 deletions(-) diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb index 9f202fe..e3cf777 100644 --- a/Ch02-statlearn-lab.ipynb +++ b/Ch02-statlearn-lab.ipynb @@ -101,12 +101,7 @@ "execution_count": 1, "id": "9e8aa21f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:05.520475Z", - "iopub.status.busy": "2023-08-22T06:59:05.520373Z", - "iopub.status.idle": "2023-08-22T06:59:05.524957Z", - "shell.execute_reply": "2023-08-22T06:59:05.524661Z" - } + "execution": {} }, "outputs": [ { @@ -134,12 +129,7 @@ "execution_count": 2, "id": "d62ec119", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:05.526569Z", - "iopub.status.busy": "2023-08-22T06:59:05.526459Z", - "iopub.status.idle": "2023-08-22T06:59:05.528480Z", - "shell.execute_reply": "2023-08-22T06:59:05.528197Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -159,12 +149,7 @@ "execution_count": 3, "id": "c64e9f4d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:05.530498Z", - "iopub.status.busy": "2023-08-22T06:59:05.530245Z", - "iopub.status.idle": "2023-08-22T06:59:05.533462Z", - "shell.execute_reply": "2023-08-22T06:59:05.533113Z" - } + "execution": {} }, "outputs": [ { @@ -199,12 +184,7 @@ "execution_count": 4, "id": "9abccc1f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:05.535049Z", - "iopub.status.busy": "2023-08-22T06:59:05.534962Z", - "iopub.status.idle": "2023-08-22T06:59:05.537010Z", - "shell.execute_reply": "2023-08-22T06:59:05.536734Z" - } + "execution": {} }, "outputs": [ { @@ -248,12 +228,7 @@ "execution_count": 5, "id": "802ca33c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:05.538667Z", - "iopub.status.busy": "2023-08-22T06:59:05.538580Z", - "iopub.status.idle": "2023-08-22T06:59:05.541098Z", - "shell.execute_reply": "2023-08-22T06:59:05.540721Z" - } + "execution": {} }, "outputs": [ { @@ -289,12 +264,7 @@ "execution_count": 6, "id": "a8c72744", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:05.542902Z", - "iopub.status.busy": "2023-08-22T06:59:05.542804Z", - "iopub.status.idle": "2023-08-22T06:59:05.545269Z", - "shell.execute_reply": "2023-08-22T06:59:05.544991Z" - } + "execution": {} }, "outputs": [ { @@ -363,12 +333,7 @@ "execution_count": 7, "id": "f1c7d1db", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:05.546995Z", - "iopub.status.busy": "2023-08-22T06:59:05.546897Z", - "iopub.status.idle": "2023-08-22T06:59:07.928068Z", - "shell.execute_reply": "2023-08-22T06:59:07.927627Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -399,12 +364,7 @@ "execution_count": 8, "id": "e2ea2bfd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.930503Z", - "iopub.status.busy": "2023-08-22T06:59:07.930338Z", - "iopub.status.idle": "2023-08-22T06:59:07.932349Z", - "shell.execute_reply": "2023-08-22T06:59:07.932076Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -438,12 +398,7 @@ "execution_count": 9, "id": "59fbf9fd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.934561Z", - "iopub.status.busy": "2023-08-22T06:59:07.934430Z", - "iopub.status.idle": "2023-08-22T06:59:07.936902Z", - "shell.execute_reply": "2023-08-22T06:59:07.936605Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -485,12 +440,7 @@ "execution_count": 10, "id": "2279437e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.939184Z", - "iopub.status.busy": "2023-08-22T06:59:07.938968Z", - "iopub.status.idle": "2023-08-22T06:59:07.941378Z", - "shell.execute_reply": "2023-08-22T06:59:07.941135Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -536,12 +486,7 @@ "execution_count": 11, "id": "75bf1b1e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.943172Z", - "iopub.status.busy": "2023-08-22T06:59:07.942965Z", - "iopub.status.idle": "2023-08-22T06:59:07.945452Z", - "shell.execute_reply": "2023-08-22T06:59:07.945026Z" - } + "execution": {} }, "outputs": [ { @@ -574,12 +519,7 @@ "execution_count": 12, "id": "58292240", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.947238Z", - "iopub.status.busy": "2023-08-22T06:59:07.947112Z", - "iopub.status.idle": "2023-08-22T06:59:07.949367Z", - "shell.execute_reply": "2023-08-22T06:59:07.949033Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -614,12 +554,7 @@ "execution_count": 13, "id": "fc5fff57", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.951095Z", - "iopub.status.busy": "2023-08-22T06:59:07.950962Z", - "iopub.status.idle": "2023-08-22T06:59:07.953323Z", - "shell.execute_reply": "2023-08-22T06:59:07.953058Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -653,12 +588,7 @@ "execution_count": 14, "id": "762562a6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.955159Z", - "iopub.status.busy": "2023-08-22T06:59:07.955029Z", - "iopub.status.idle": "2023-08-22T06:59:07.957291Z", - "shell.execute_reply": "2023-08-22T06:59:07.956944Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -679,12 +609,7 @@ "execution_count": 15, "id": "66d2b82a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.959022Z", - "iopub.status.busy": "2023-08-22T06:59:07.958921Z", - "iopub.status.idle": "2023-08-22T06:59:07.961144Z", - "shell.execute_reply": "2023-08-22T06:59:07.960798Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -717,12 +642,7 @@ "execution_count": 16, "id": "89881402", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.963075Z", - "iopub.status.busy": "2023-08-22T06:59:07.962934Z", - "iopub.status.idle": "2023-08-22T06:59:07.965265Z", - "shell.execute_reply": "2023-08-22T06:59:07.964978Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -760,12 +680,7 @@ "execution_count": 17, "id": "0572d3f6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.967468Z", - "iopub.status.busy": "2023-08-22T06:59:07.967139Z", - "iopub.status.idle": "2023-08-22T06:59:07.969644Z", - "shell.execute_reply": "2023-08-22T06:59:07.969386Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -798,12 +713,7 @@ "execution_count": 18, "id": "33b10a6f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.971652Z", - "iopub.status.busy": "2023-08-22T06:59:07.971489Z", - "iopub.status.idle": "2023-08-22T06:59:07.974229Z", - "shell.execute_reply": "2023-08-22T06:59:07.973900Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -844,12 +754,7 @@ "execution_count": 19, "id": "a32716db", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.976214Z", - "iopub.status.busy": "2023-08-22T06:59:07.976066Z", - "iopub.status.idle": "2023-08-22T06:59:07.978551Z", - "shell.execute_reply": "2023-08-22T06:59:07.978148Z" - } + "execution": {} }, "outputs": [ { @@ -895,12 +800,7 @@ "execution_count": 20, "id": "3db6e1cf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.980462Z", - "iopub.status.busy": "2023-08-22T06:59:07.980329Z", - "iopub.status.idle": "2023-08-22T06:59:07.982878Z", - "shell.execute_reply": "2023-08-22T06:59:07.982438Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -933,12 +833,7 @@ "execution_count": 21, "id": "e15c753f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.984961Z", - "iopub.status.busy": "2023-08-22T06:59:07.984816Z", - "iopub.status.idle": "2023-08-22T06:59:07.987101Z", - "shell.execute_reply": "2023-08-22T06:59:07.986805Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -974,12 +869,7 @@ "execution_count": 22, "id": "91c6e7d8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.989269Z", - "iopub.status.busy": "2023-08-22T06:59:07.988940Z", - "iopub.status.idle": "2023-08-22T06:59:07.991567Z", - "shell.execute_reply": "2023-08-22T06:59:07.991233Z" - } + "execution": {} }, "outputs": [ { @@ -1032,12 +922,7 @@ "execution_count": 23, "id": "59d95dce", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:07.993423Z", - "iopub.status.busy": "2023-08-22T06:59:07.993299Z", - "iopub.status.idle": "2023-08-22T06:59:08.133462Z", - "shell.execute_reply": "2023-08-22T06:59:08.132910Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1072,12 +957,7 @@ "execution_count": 24, "id": "a6fde9af", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.135655Z", - "iopub.status.busy": "2023-08-22T06:59:08.135507Z", - "iopub.status.idle": "2023-08-22T06:59:08.138481Z", - "shell.execute_reply": "2023-08-22T06:59:08.138180Z" - } + "execution": {} }, "outputs": [ { @@ -1116,12 +996,7 @@ "execution_count": 25, "id": "fadb6b45", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.140173Z", - "iopub.status.busy": "2023-08-22T06:59:08.140051Z", - "iopub.status.idle": "2023-08-22T06:59:08.142777Z", - "shell.execute_reply": "2023-08-22T06:59:08.142404Z" - } + "execution": {} }, "outputs": [ { @@ -1153,12 +1028,7 @@ "execution_count": 26, "id": "fda3134b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.144782Z", - "iopub.status.busy": "2023-08-22T06:59:08.144640Z", - "iopub.status.idle": "2023-08-22T06:59:08.147297Z", - "shell.execute_reply": "2023-08-22T06:59:08.146996Z" - } + "execution": {} }, "outputs": [ { @@ -1189,12 +1059,7 @@ "execution_count": 27, "id": "52eb335b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.148995Z", - "iopub.status.busy": "2023-08-22T06:59:08.148870Z", - "iopub.status.idle": "2023-08-22T06:59:08.151571Z", - "shell.execute_reply": "2023-08-22T06:59:08.151251Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1236,12 +1101,7 @@ "execution_count": 28, "id": "ac5e9d29", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.154113Z", - "iopub.status.busy": "2023-08-22T06:59:08.153958Z", - "iopub.status.idle": "2023-08-22T06:59:08.157534Z", - "shell.execute_reply": "2023-08-22T06:59:08.156973Z" - } + "execution": {} }, "outputs": [ { @@ -1282,12 +1142,7 @@ "execution_count": 29, "id": "55fa905e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.159589Z", - "iopub.status.busy": "2023-08-22T06:59:08.159437Z", - "iopub.status.idle": "2023-08-22T06:59:08.161722Z", - "shell.execute_reply": "2023-08-22T06:59:08.161369Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1309,12 +1164,7 @@ "execution_count": 30, "id": "fde0dc19", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.164153Z", - "iopub.status.busy": "2023-08-22T06:59:08.163979Z", - "iopub.status.idle": "2023-08-22T06:59:08.168094Z", - "shell.execute_reply": "2023-08-22T06:59:08.167676Z" - } + "execution": {} }, "outputs": [ { @@ -1349,12 +1199,7 @@ "execution_count": 31, "id": "5099cf54", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.169980Z", - "iopub.status.busy": "2023-08-22T06:59:08.169865Z", - "iopub.status.idle": "2023-08-22T06:59:08.173468Z", - "shell.execute_reply": "2023-08-22T06:59:08.172857Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1400,12 +1245,7 @@ "execution_count": 32, "id": "9d8074e5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.175381Z", - "iopub.status.busy": "2023-08-22T06:59:08.175239Z", - "iopub.status.idle": "2023-08-22T06:59:08.177926Z", - "shell.execute_reply": "2023-08-22T06:59:08.177603Z" - } + "execution": {} }, "outputs": [ { @@ -1446,12 +1286,7 @@ "execution_count": 33, "id": "e98472df", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.179784Z", - "iopub.status.busy": "2023-08-22T06:59:08.179647Z", - "iopub.status.idle": "2023-08-22T06:59:08.182540Z", - "shell.execute_reply": "2023-08-22T06:59:08.182002Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1485,12 +1320,7 @@ "execution_count": 34, "id": "8c2784fd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.184240Z", - "iopub.status.busy": "2023-08-22T06:59:08.184117Z", - "iopub.status.idle": "2023-08-22T06:59:08.186691Z", - "shell.execute_reply": "2023-08-22T06:59:08.186367Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1523,12 +1353,7 @@ "execution_count": 35, "id": "7e7205f2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.188421Z", - "iopub.status.busy": "2023-08-22T06:59:08.188291Z", - "iopub.status.idle": "2023-08-22T06:59:08.191242Z", - "shell.execute_reply": "2023-08-22T06:59:08.190935Z" - } + "execution": {} }, "outputs": [ { @@ -1560,12 +1385,7 @@ "execution_count": 36, "id": "fce06849", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.192889Z", - "iopub.status.busy": "2023-08-22T06:59:08.192762Z", - "iopub.status.idle": "2023-08-22T06:59:08.195517Z", - "shell.execute_reply": "2023-08-22T06:59:08.195194Z" - } + "execution": {} }, "outputs": [ { @@ -1606,12 +1426,7 @@ "execution_count": 37, "id": "1403ff7a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.197212Z", - "iopub.status.busy": "2023-08-22T06:59:08.197063Z", - "iopub.status.idle": "2023-08-22T06:59:08.199463Z", - "shell.execute_reply": "2023-08-22T06:59:08.199082Z" - } + "execution": {} }, "outputs": [ { @@ -1642,12 +1457,7 @@ "execution_count": 38, "id": "7e9255ba", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.201135Z", - "iopub.status.busy": "2023-08-22T06:59:08.201013Z", - "iopub.status.idle": "2023-08-22T06:59:08.203278Z", - "shell.execute_reply": "2023-08-22T06:59:08.202984Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1712,12 +1522,7 @@ "execution_count": 39, "id": "8236e5f7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:08.205399Z", - "iopub.status.busy": "2023-08-22T06:59:08.205262Z", - "iopub.status.idle": "2023-08-22T06:59:09.267032Z", - "shell.execute_reply": "2023-08-22T06:59:09.266627Z" - } + "execution": {} }, "outputs": [ { @@ -1754,12 +1559,7 @@ "execution_count": 40, "id": "ddc9ed4f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.269192Z", - "iopub.status.busy": "2023-08-22T06:59:09.269015Z", - "iopub.status.idle": "2023-08-22T06:59:09.336385Z", - "shell.execute_reply": "2023-08-22T06:59:09.335994Z" - } + "execution": {} }, "outputs": [ { @@ -1792,12 +1592,7 @@ "execution_count": 41, "id": "c64ed600", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.338601Z", - "iopub.status.busy": "2023-08-22T06:59:09.338466Z", - "iopub.status.idle": "2023-08-22T06:59:09.408187Z", - "shell.execute_reply": "2023-08-22T06:59:09.407850Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1840,12 +1635,7 @@ "execution_count": 42, "id": "bc6245e2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.410036Z", - "iopub.status.busy": "2023-08-22T06:59:09.409914Z", - "iopub.status.idle": "2023-08-22T06:59:09.481803Z", - "shell.execute_reply": "2023-08-22T06:59:09.481516Z" - } + "execution": {} }, "outputs": [ { @@ -1880,12 +1670,7 @@ "execution_count": 43, "id": "2454807b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.483769Z", - "iopub.status.busy": "2023-08-22T06:59:09.483410Z", - "iopub.status.idle": "2023-08-22T06:59:09.583871Z", - "shell.execute_reply": "2023-08-22T06:59:09.583567Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1943,12 +1728,7 @@ "execution_count": 44, "id": "1e18a793", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.585676Z", - "iopub.status.busy": "2023-08-22T06:59:09.585555Z", - "iopub.status.idle": "2023-08-22T06:59:09.674249Z", - "shell.execute_reply": "2023-08-22T06:59:09.673902Z" - } + "execution": {} }, "outputs": [ { @@ -1984,12 +1764,7 @@ "execution_count": 45, "id": "aec3f009", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.676005Z", - "iopub.status.busy": "2023-08-22T06:59:09.675879Z", - "iopub.status.idle": "2023-08-22T06:59:09.733152Z", - "shell.execute_reply": "2023-08-22T06:59:09.732799Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2038,12 +1813,7 @@ "execution_count": 46, "id": "2cbc7fd4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.734976Z", - "iopub.status.busy": "2023-08-22T06:59:09.734855Z", - "iopub.status.idle": "2023-08-22T06:59:09.987982Z", - "shell.execute_reply": "2023-08-22T06:59:09.987518Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2078,12 +1848,7 @@ "execution_count": 47, "id": "702f80d9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:09.989943Z", - "iopub.status.busy": "2023-08-22T06:59:09.989799Z", - "iopub.status.idle": "2023-08-22T06:59:10.164367Z", - "shell.execute_reply": "2023-08-22T06:59:10.164050Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2131,12 +1896,7 @@ "execution_count": 48, "id": "5493d229", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:10.166485Z", - "iopub.status.busy": "2023-08-22T06:59:10.166339Z", - "iopub.status.idle": "2023-08-22T06:59:11.473366Z", - "shell.execute_reply": "2023-08-22T06:59:11.473053Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -2158,12 +1918,7 @@ "execution_count": 49, "id": "bd07af12", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:11.475355Z", - "iopub.status.busy": "2023-08-22T06:59:11.475181Z", - "iopub.status.idle": "2023-08-22T06:59:11.696662Z", - "shell.execute_reply": "2023-08-22T06:59:11.696345Z" - } + "execution": {} }, "outputs": [ { @@ -2208,12 +1963,7 @@ "execution_count": 50, "id": "01019508", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:11.698588Z", - "iopub.status.busy": "2023-08-22T06:59:11.698446Z", - "iopub.status.idle": "2023-08-22T06:59:11.986555Z", - "shell.execute_reply": "2023-08-22T06:59:11.986111Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2249,12 +1999,7 @@ "execution_count": 51, "id": "7d08992f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:11.988767Z", - "iopub.status.busy": "2023-08-22T06:59:11.988614Z", - "iopub.status.idle": "2023-08-22T06:59:12.104022Z", - "shell.execute_reply": "2023-08-22T06:59:12.103708Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2295,12 +2040,7 @@ "execution_count": 52, "id": "1f89d704", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.105969Z", - "iopub.status.busy": "2023-08-22T06:59:12.105871Z", - "iopub.status.idle": "2023-08-22T06:59:12.198374Z", - "shell.execute_reply": "2023-08-22T06:59:12.197945Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2343,12 +2083,7 @@ "execution_count": 53, "id": "cd971131", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.200827Z", - "iopub.status.busy": "2023-08-22T06:59:12.200655Z", - "iopub.status.idle": "2023-08-22T06:59:12.203494Z", - "shell.execute_reply": "2023-08-22T06:59:12.203195Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2383,12 +2118,7 @@ "execution_count": 54, "id": "aa630d16", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.205484Z", - "iopub.status.busy": "2023-08-22T06:59:12.205351Z", - "iopub.status.idle": "2023-08-22T06:59:12.207885Z", - "shell.execute_reply": "2023-08-22T06:59:12.207564Z" - } + "execution": {} }, "outputs": [ { @@ -2424,12 +2154,7 @@ "execution_count": 55, "id": "89955ee2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.209574Z", - "iopub.status.busy": "2023-08-22T06:59:12.209454Z", - "iopub.status.idle": "2023-08-22T06:59:12.212189Z", - "shell.execute_reply": "2023-08-22T06:59:12.211670Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2462,12 +2187,7 @@ "execution_count": 56, "id": "517f592d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.214090Z", - "iopub.status.busy": "2023-08-22T06:59:12.213954Z", - "iopub.status.idle": "2023-08-22T06:59:12.216634Z", - "shell.execute_reply": "2023-08-22T06:59:12.216229Z" - } + "execution": {} }, "outputs": [ { @@ -2529,12 +2249,7 @@ "execution_count": 57, "id": "35927abd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.218701Z", - "iopub.status.busy": "2023-08-22T06:59:12.218551Z", - "iopub.status.idle": "2023-08-22T06:59:12.221779Z", - "shell.execute_reply": "2023-08-22T06:59:12.221078Z" - } + "execution": {} }, "outputs": [ { @@ -2570,12 +2285,7 @@ "execution_count": 58, "id": "78ee7f5b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.223849Z", - "iopub.status.busy": "2023-08-22T06:59:12.223730Z", - "iopub.status.idle": "2023-08-22T06:59:12.226375Z", - "shell.execute_reply": "2023-08-22T06:59:12.226073Z" - } + "execution": {} }, "outputs": [ { @@ -2611,12 +2321,7 @@ "execution_count": 59, "id": "16212696", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.228118Z", - "iopub.status.busy": "2023-08-22T06:59:12.227993Z", - "iopub.status.idle": "2023-08-22T06:59:12.230254Z", - "shell.execute_reply": "2023-08-22T06:59:12.229993Z" - } + "execution": {} }, "outputs": [ { @@ -2650,12 +2355,7 @@ "execution_count": 60, "id": "d5f473d2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.231967Z", - "iopub.status.busy": "2023-08-22T06:59:12.231858Z", - "iopub.status.idle": "2023-08-22T06:59:12.234317Z", - "shell.execute_reply": "2023-08-22T06:59:12.234032Z" - } + "execution": {} }, "outputs": [ { @@ -2691,12 +2391,7 @@ "execution_count": 61, "id": "c89646d6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.236331Z", - "iopub.status.busy": "2023-08-22T06:59:12.236194Z", - "iopub.status.idle": "2023-08-22T06:59:12.238788Z", - "shell.execute_reply": "2023-08-22T06:59:12.238448Z" - } + "execution": {} }, "outputs": [ { @@ -2727,12 +2422,7 @@ "execution_count": 62, "id": "87f6b4f2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.240562Z", - "iopub.status.busy": "2023-08-22T06:59:12.240434Z", - "iopub.status.idle": "2023-08-22T06:59:12.242956Z", - "shell.execute_reply": "2023-08-22T06:59:12.242696Z" - } + "execution": {} }, "outputs": [ { @@ -2763,12 +2453,7 @@ "execution_count": 63, "id": "5da5bda8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.244576Z", - "iopub.status.busy": "2023-08-22T06:59:12.244457Z", - "iopub.status.idle": "2023-08-22T06:59:12.267725Z", - "shell.execute_reply": "2023-08-22T06:59:12.267329Z" - } + "execution": {} }, "outputs": [ { @@ -2802,12 +2487,7 @@ "execution_count": 64, "id": "ac48a95b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.269754Z", - "iopub.status.busy": "2023-08-22T06:59:12.269614Z", - "iopub.status.idle": "2023-08-22T06:59:12.272265Z", - "shell.execute_reply": "2023-08-22T06:59:12.271962Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2851,12 +2531,7 @@ "execution_count": 65, "id": "ee195cc4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.273998Z", - "iopub.status.busy": "2023-08-22T06:59:12.273857Z", - "iopub.status.idle": "2023-08-22T06:59:12.276565Z", - "shell.execute_reply": "2023-08-22T06:59:12.276235Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2894,12 +2569,7 @@ "execution_count": 66, "id": "48917bb5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.278649Z", - "iopub.status.busy": "2023-08-22T06:59:12.278494Z", - "iopub.status.idle": "2023-08-22T06:59:12.281280Z", - "shell.execute_reply": "2023-08-22T06:59:12.280843Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2964,12 +2634,7 @@ "execution_count": 67, "id": "5d4caf22", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.282968Z", - "iopub.status.busy": "2023-08-22T06:59:12.282854Z", - "iopub.status.idle": "2023-08-22T06:59:12.285234Z", - "shell.execute_reply": "2023-08-22T06:59:12.284961Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3002,12 +2667,7 @@ "execution_count": 68, "id": "348820e3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.287427Z", - "iopub.status.busy": "2023-08-22T06:59:12.287184Z", - "iopub.status.idle": "2023-08-22T06:59:12.289915Z", - "shell.execute_reply": "2023-08-22T06:59:12.289589Z" - } + "execution": {} }, "outputs": [ { @@ -3041,12 +2701,7 @@ "execution_count": 69, "id": "4aafe45b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.291767Z", - "iopub.status.busy": "2023-08-22T06:59:12.291634Z", - "iopub.status.idle": "2023-08-22T06:59:12.294302Z", - "shell.execute_reply": "2023-08-22T06:59:12.293967Z" - } + "execution": {} }, "outputs": [ { @@ -3087,12 +2742,7 @@ "execution_count": 70, "id": "1be6a588", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.296189Z", - "iopub.status.busy": "2023-08-22T06:59:12.296052Z", - "iopub.status.idle": "2023-08-22T06:59:12.298784Z", - "shell.execute_reply": "2023-08-22T06:59:12.298383Z" - } + "execution": {} }, "outputs": [ { @@ -3126,12 +2776,7 @@ "execution_count": 71, "id": "e83da57b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.300802Z", - "iopub.status.busy": "2023-08-22T06:59:12.300663Z", - "iopub.status.idle": "2023-08-22T06:59:12.303240Z", - "shell.execute_reply": "2023-08-22T06:59:12.302865Z" - } + "execution": {} }, "outputs": [ { @@ -3173,12 +2818,7 @@ "execution_count": 72, "id": "09675294", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.305143Z", - "iopub.status.busy": "2023-08-22T06:59:12.305025Z", - "iopub.status.idle": "2023-08-22T06:59:12.307529Z", - "shell.execute_reply": "2023-08-22T06:59:12.307219Z" - } + "execution": {} }, "outputs": [ { @@ -3213,12 +2853,7 @@ "execution_count": 73, "id": "a85614e4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.309554Z", - "iopub.status.busy": "2023-08-22T06:59:12.309418Z", - "iopub.status.idle": "2023-08-22T06:59:12.311737Z", - "shell.execute_reply": "2023-08-22T06:59:12.311441Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3305,12 +2940,7 @@ "execution_count": 74, "id": "ff81e644", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:12.313549Z", - "iopub.status.busy": "2023-08-22T06:59:12.313428Z", - "iopub.status.idle": "2023-08-22T06:59:14.571524Z", - "shell.execute_reply": "2023-08-22T06:59:14.571235Z" - } + "execution": {} }, "outputs": [ { @@ -3537,12 +3167,7 @@ "execution_count": 75, "id": "5b45aa7f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.573445Z", - "iopub.status.busy": "2023-08-22T06:59:14.573227Z", - "iopub.status.idle": "2023-08-22T06:59:14.576905Z", - "shell.execute_reply": "2023-08-22T06:59:14.576624Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3574,12 +3199,7 @@ "execution_count": 76, "id": "413f626a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.578771Z", - "iopub.status.busy": "2023-08-22T06:59:14.578606Z", - "iopub.status.idle": "2023-08-22T06:59:14.581727Z", - "shell.execute_reply": "2023-08-22T06:59:14.581462Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3625,12 +3245,7 @@ "execution_count": 77, "id": "57b86346", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.583287Z", - "iopub.status.busy": "2023-08-22T06:59:14.583182Z", - "iopub.status.idle": "2023-08-22T06:59:14.585660Z", - "shell.execute_reply": "2023-08-22T06:59:14.585401Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3686,12 +3301,7 @@ "execution_count": 78, "id": "a9698b26", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.587247Z", - "iopub.status.busy": "2023-08-22T06:59:14.587134Z", - "iopub.status.idle": "2023-08-22T06:59:14.591564Z", - "shell.execute_reply": "2023-08-22T06:59:14.591275Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3727,12 +3337,7 @@ "execution_count": 79, "id": "4877cb2c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.593387Z", - "iopub.status.busy": "2023-08-22T06:59:14.593233Z", - "iopub.status.idle": "2023-08-22T06:59:14.595399Z", - "shell.execute_reply": "2023-08-22T06:59:14.595111Z" - } + "execution": {} }, "outputs": [ { @@ -3766,12 +3371,7 @@ "execution_count": 80, "id": "2ba1d33d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.596985Z", - "iopub.status.busy": "2023-08-22T06:59:14.596880Z", - "iopub.status.idle": "2023-08-22T06:59:14.599760Z", - "shell.execute_reply": "2023-08-22T06:59:14.599435Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3806,12 +3406,7 @@ "execution_count": 81, "id": "3d03baab", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.602110Z", - "iopub.status.busy": "2023-08-22T06:59:14.601973Z", - "iopub.status.idle": "2023-08-22T06:59:14.604410Z", - "shell.execute_reply": "2023-08-22T06:59:14.604070Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3850,12 +3445,7 @@ "execution_count": 82, "id": "410b4dd7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.606098Z", - "iopub.status.busy": "2023-08-22T06:59:14.605987Z", - "iopub.status.idle": "2023-08-22T06:59:14.610632Z", - "shell.execute_reply": "2023-08-22T06:59:14.610234Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3966,12 +3556,7 @@ "execution_count": 83, "id": "3540804d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.612402Z", - "iopub.status.busy": "2023-08-22T06:59:14.612271Z", - "iopub.status.idle": "2023-08-22T06:59:14.625177Z", - "shell.execute_reply": "2023-08-22T06:59:14.624839Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -4853,12 +4438,7 @@ "execution_count": 84, "id": "66d174f1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.626829Z", - "iopub.status.busy": "2023-08-22T06:59:14.626719Z", - "iopub.status.idle": "2023-08-22T06:59:14.631422Z", - "shell.execute_reply": "2023-08-22T06:59:14.631104Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -4988,12 +4568,7 @@ "execution_count": 85, "id": "52789c77", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.633211Z", - "iopub.status.busy": "2023-08-22T06:59:14.633074Z", - "iopub.status.idle": "2023-08-22T06:59:14.635511Z", - "shell.execute_reply": "2023-08-22T06:59:14.635190Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5029,12 +4604,7 @@ "execution_count": 86, "id": "d83650bf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.637090Z", - "iopub.status.busy": "2023-08-22T06:59:14.636984Z", - "iopub.status.idle": "2023-08-22T06:59:14.643250Z", - "shell.execute_reply": "2023-08-22T06:59:14.642921Z" - } + "execution": {} }, "outputs": [ { @@ -5253,12 +4823,7 @@ "execution_count": 87, "id": "880d79d9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.644959Z", - "iopub.status.busy": "2023-08-22T06:59:14.644832Z", - "iopub.status.idle": "2023-08-22T06:59:14.647067Z", - "shell.execute_reply": "2023-08-22T06:59:14.646780Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5296,12 +4861,7 @@ "execution_count": 88, "id": "c01f4095", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.648616Z", - "iopub.status.busy": "2023-08-22T06:59:14.648506Z", - "iopub.status.idle": "2023-08-22T06:59:14.653020Z", - "shell.execute_reply": "2023-08-22T06:59:14.652673Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5409,12 +4969,7 @@ "execution_count": 89, "id": "a4202eb8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.654821Z", - "iopub.status.busy": "2023-08-22T06:59:14.654708Z", - "iopub.status.idle": "2023-08-22T06:59:14.658952Z", - "shell.execute_reply": "2023-08-22T06:59:14.658676Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5521,12 +5076,7 @@ "execution_count": 90, "id": "948b2d07", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.660688Z", - "iopub.status.busy": "2023-08-22T06:59:14.660582Z", - "iopub.status.idle": "2023-08-22T06:59:14.665683Z", - "shell.execute_reply": "2023-08-22T06:59:14.665320Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5675,12 +5225,7 @@ "execution_count": 91, "id": "1cfdcc5c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.667792Z", - "iopub.status.busy": "2023-08-22T06:59:14.667649Z", - "iopub.status.idle": "2023-08-22T06:59:14.672086Z", - "shell.execute_reply": "2023-08-22T06:59:14.671668Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5762,12 +5307,7 @@ "execution_count": 92, "id": "fd9c5cda", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.674155Z", - "iopub.status.busy": "2023-08-22T06:59:14.674020Z", - "iopub.status.idle": "2023-08-22T06:59:14.677900Z", - "shell.execute_reply": "2023-08-22T06:59:14.677570Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5855,12 +5395,7 @@ "execution_count": 93, "id": "6d431cb5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.679838Z", - "iopub.status.busy": "2023-08-22T06:59:14.679694Z", - "iopub.status.idle": "2023-08-22T06:59:14.685743Z", - "shell.execute_reply": "2023-08-22T06:59:14.685309Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -6275,12 +5810,7 @@ "execution_count": 94, "id": "fac41ce1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.687552Z", - "iopub.status.busy": "2023-08-22T06:59:14.687421Z", - "iopub.status.idle": "2023-08-22T06:59:14.693477Z", - "shell.execute_reply": "2023-08-22T06:59:14.693157Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -6699,12 +6229,7 @@ "execution_count": 95, "id": "b0885654", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.695271Z", - "iopub.status.busy": "2023-08-22T06:59:14.695116Z", - "iopub.status.idle": "2023-08-22T06:59:14.700210Z", - "shell.execute_reply": "2023-08-22T06:59:14.699919Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -6979,12 +6504,7 @@ "execution_count": 96, "id": "213945a6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.702075Z", - "iopub.status.busy": "2023-08-22T06:59:14.701942Z", - "iopub.status.idle": "2023-08-22T06:59:14.708849Z", - "shell.execute_reply": "2023-08-22T06:59:14.708468Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7370,12 +6890,7 @@ "execution_count": 97, "id": "a3c4060a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.710560Z", - "iopub.status.busy": "2023-08-22T06:59:14.710418Z", - "iopub.status.idle": "2023-08-22T06:59:14.712662Z", - "shell.execute_reply": "2023-08-22T06:59:14.712379Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7414,12 +6929,7 @@ "execution_count": 98, "id": "f2bffb69", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.714548Z", - "iopub.status.busy": "2023-08-22T06:59:14.714416Z", - "iopub.status.idle": "2023-08-22T06:59:14.716724Z", - "shell.execute_reply": "2023-08-22T06:59:14.716275Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7466,12 +6976,7 @@ "execution_count": 99, "id": "ee827a53", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.718538Z", - "iopub.status.busy": "2023-08-22T06:59:14.718418Z", - "iopub.status.idle": "2023-08-22T06:59:14.720778Z", - "shell.execute_reply": "2023-08-22T06:59:14.720410Z" - } + "execution": {} }, "outputs": [ { @@ -7521,12 +7026,7 @@ "execution_count": 100, "id": "3a097fbc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.722745Z", - "iopub.status.busy": "2023-08-22T06:59:14.722445Z", - "iopub.status.idle": "2023-08-22T06:59:14.727643Z", - "shell.execute_reply": "2023-08-22T06:59:14.727302Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -7617,12 +7117,7 @@ "execution_count": 101, "id": "e064e170", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.729283Z", - "iopub.status.busy": "2023-08-22T06:59:14.729148Z", - "iopub.status.idle": "2023-08-22T06:59:14.732019Z", - "shell.execute_reply": "2023-08-22T06:59:14.731599Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7674,12 +7169,7 @@ "execution_count": 102, "id": "c915ca52", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.733916Z", - "iopub.status.busy": "2023-08-22T06:59:14.733787Z", - "iopub.status.idle": "2023-08-22T06:59:14.823121Z", - "shell.execute_reply": "2023-08-22T06:59:14.822788Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7723,12 +7213,7 @@ "execution_count": 103, "id": "65cd6d02", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.825251Z", - "iopub.status.busy": "2023-08-22T06:59:14.825113Z", - "iopub.status.idle": "2023-08-22T06:59:14.905789Z", - "shell.execute_reply": "2023-08-22T06:59:14.905480Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7765,12 +7250,7 @@ "execution_count": 104, "id": "76b5c0b1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.907636Z", - "iopub.status.busy": "2023-08-22T06:59:14.907511Z", - "iopub.status.idle": "2023-08-22T06:59:14.997060Z", - "shell.execute_reply": "2023-08-22T06:59:14.996751Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7805,12 +7285,7 @@ "execution_count": 105, "id": "183a2c2b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:14.998844Z", - "iopub.status.busy": "2023-08-22T06:59:14.998722Z", - "iopub.status.idle": "2023-08-22T06:59:15.033680Z", - "shell.execute_reply": "2023-08-22T06:59:15.033292Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -7836,12 +7311,7 @@ "execution_count": 106, "id": "75fbb981", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:15.035870Z", - "iopub.status.busy": "2023-08-22T06:59:15.035723Z", - "iopub.status.idle": "2023-08-22T06:59:15.198431Z", - "shell.execute_reply": "2023-08-22T06:59:15.198047Z" - } + "execution": {} }, "outputs": [ { @@ -7884,12 +7354,7 @@ "execution_count": 107, "id": "55b3a1cc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:15.200753Z", - "iopub.status.busy": "2023-08-22T06:59:15.200526Z", - "iopub.status.idle": "2023-08-22T06:59:15.203898Z", - "shell.execute_reply": "2023-08-22T06:59:15.203556Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7923,12 +7388,7 @@ "execution_count": 108, "id": "f3d88794", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:15.205746Z", - "iopub.status.busy": "2023-08-22T06:59:15.205652Z", - "iopub.status.idle": "2023-08-22T06:59:15.305674Z", - "shell.execute_reply": "2023-08-22T06:59:15.305240Z" - } + "execution": {} }, "outputs": [ { @@ -7960,12 +7420,7 @@ "execution_count": 109, "id": "eea49f5b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:15.307641Z", - "iopub.status.busy": "2023-08-22T06:59:15.307491Z", - "iopub.status.idle": "2023-08-22T06:59:15.405217Z", - "shell.execute_reply": "2023-08-22T06:59:15.404785Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7998,12 +7453,7 @@ "execution_count": 110, "id": "d5bcfff8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:15.407321Z", - "iopub.status.busy": "2023-08-22T06:59:15.407190Z", - "iopub.status.idle": "2023-08-22T06:59:15.503021Z", - "shell.execute_reply": "2023-08-22T06:59:15.502607Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8040,12 +7490,7 @@ "execution_count": 111, "id": "edb66cae", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:15.504880Z", - "iopub.status.busy": "2023-08-22T06:59:15.504746Z", - "iopub.status.idle": "2023-08-22T06:59:16.457367Z", - "shell.execute_reply": "2023-08-22T06:59:16.457033Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8078,12 +7523,7 @@ "execution_count": 112, "id": "4f5d25d9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:16.459211Z", - "iopub.status.busy": "2023-08-22T06:59:16.459087Z", - "iopub.status.idle": "2023-08-22T06:59:16.684825Z", - "shell.execute_reply": "2023-08-22T06:59:16.684501Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8117,12 +7557,7 @@ "execution_count": 113, "id": "ce7b23e2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:16.686592Z", - "iopub.status.busy": "2023-08-22T06:59:16.686475Z", - "iopub.status.idle": "2023-08-22T06:59:16.692382Z", - "shell.execute_reply": "2023-08-22T06:59:16.692013Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8230,12 +7665,7 @@ "execution_count": 114, "id": "a6545d2f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:16.694320Z", - "iopub.status.busy": "2023-08-22T06:59:16.694191Z", - "iopub.status.idle": "2023-08-22T06:59:16.698368Z", - "shell.execute_reply": "2023-08-22T06:59:16.698098Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ diff --git a/Ch03-linreg-lab.ipynb b/Ch03-linreg-lab.ipynb index 78a516a..4d38fa6 100644 --- a/Ch03-linreg-lab.ipynb +++ b/Ch03-linreg-lab.ipynb @@ -26,12 +26,7 @@ "execution_count": 1, "id": "b18c1628", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:19.963803Z", - "iopub.status.busy": "2023-08-22T06:59:19.963695Z", - "iopub.status.idle": "2023-08-22T06:59:20.384511Z", - "shell.execute_reply": "2023-08-22T06:59:20.384219Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -59,12 +54,7 @@ "execution_count": 2, "id": "a10a1d7d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:20.387117Z", - "iopub.status.busy": "2023-08-22T06:59:20.386932Z", - "iopub.status.idle": "2023-08-22T06:59:27.510372Z", - "shell.execute_reply": "2023-08-22T06:59:27.510076Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -92,12 +82,7 @@ "execution_count": 3, "id": "756c0524", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:27.512280Z", - "iopub.status.busy": "2023-08-22T06:59:27.512129Z", - "iopub.status.idle": "2023-08-22T06:59:27.514850Z", - "shell.execute_reply": "2023-08-22T06:59:27.514531Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -123,12 +108,7 @@ "execution_count": 4, "id": "2c370a6e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:27.516720Z", - "iopub.status.busy": "2023-08-22T06:59:27.516584Z", - "iopub.status.idle": "2023-08-22T06:59:30.213761Z", - "shell.execute_reply": "2023-08-22T06:59:30.213403Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -155,12 +135,7 @@ "execution_count": 5, "id": "43bcb3a9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.215847Z", - "iopub.status.busy": "2023-08-22T06:59:30.215679Z", - "iopub.status.idle": "2023-08-22T06:59:30.219200Z", - "shell.execute_reply": "2023-08-22T06:59:30.218854Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -236,12 +211,7 @@ "execution_count": 6, "id": "cb81bfb0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.220842Z", - "iopub.status.busy": "2023-08-22T06:59:30.220745Z", - "iopub.status.idle": "2023-08-22T06:59:30.223794Z", - "shell.execute_reply": "2023-08-22T06:59:30.223517Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -439,12 +409,7 @@ "execution_count": 7, "id": "29e9bdab", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.225458Z", - "iopub.status.busy": "2023-08-22T06:59:30.225336Z", - "iopub.status.idle": "2023-08-22T06:59:30.227667Z", - "shell.execute_reply": "2023-08-22T06:59:30.227385Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -497,12 +462,7 @@ "execution_count": 8, "id": "72a15de5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.229351Z", - "iopub.status.busy": "2023-08-22T06:59:30.229229Z", - "iopub.status.idle": "2023-08-22T06:59:30.233978Z", - "shell.execute_reply": "2023-08-22T06:59:30.233738Z" - } + "execution": {} }, "outputs": [ { @@ -541,12 +501,7 @@ "execution_count": 9, "id": "26837cfe", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.235692Z", - "iopub.status.busy": "2023-08-22T06:59:30.235572Z", - "iopub.status.idle": "2023-08-22T06:59:30.240548Z", - "shell.execute_reply": "2023-08-22T06:59:30.240254Z" - } + "execution": {} }, "outputs": [ { @@ -631,12 +586,7 @@ "execution_count": 10, "id": "c70cbdb5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.242180Z", - "iopub.status.busy": "2023-08-22T06:59:30.242082Z", - "iopub.status.idle": "2023-08-22T06:59:30.244423Z", - "shell.execute_reply": "2023-08-22T06:59:30.244043Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -666,12 +616,7 @@ "execution_count": 11, "id": "6f2d7c78", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.246386Z", - "iopub.status.busy": "2023-08-22T06:59:30.246241Z", - "iopub.status.idle": "2023-08-22T06:59:30.304643Z", - "shell.execute_reply": "2023-08-22T06:59:30.304342Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -777,12 +722,7 @@ "execution_count": 12, "id": "cdec4294", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.306725Z", - "iopub.status.busy": "2023-08-22T06:59:30.306537Z", - "iopub.status.idle": "2023-08-22T06:59:30.313928Z", - "shell.execute_reply": "2023-08-22T06:59:30.313612Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -872,12 +812,7 @@ "execution_count": 13, "id": "edf2efcb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.316046Z", - "iopub.status.busy": "2023-08-22T06:59:30.315927Z", - "iopub.status.idle": "2023-08-22T06:59:30.322463Z", - "shell.execute_reply": "2023-08-22T06:59:30.322137Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -976,12 +911,7 @@ "execution_count": 14, "id": "49fc8992", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.324453Z", - "iopub.status.busy": "2023-08-22T06:59:30.324311Z", - "iopub.status.idle": "2023-08-22T06:59:30.334219Z", - "shell.execute_reply": "2023-08-22T06:59:30.333881Z" - } + "execution": {} }, "outputs": [ { @@ -1132,12 +1062,7 @@ "execution_count": 15, "id": "6d0f4c3a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.336071Z", - "iopub.status.busy": "2023-08-22T06:59:30.335936Z", - "iopub.status.idle": "2023-08-22T06:59:30.338769Z", - "shell.execute_reply": "2023-08-22T06:59:30.338416Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1175,12 +1100,7 @@ "execution_count": 16, "id": "132ffded", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.340661Z", - "iopub.status.busy": "2023-08-22T06:59:30.340521Z", - "iopub.status.idle": "2023-08-22T06:59:30.345095Z", - "shell.execute_reply": "2023-08-22T06:59:30.344737Z" - } + "execution": {} }, "outputs": [ { @@ -1259,12 +1179,7 @@ "execution_count": 17, "id": "b654a050", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.346874Z", - "iopub.status.busy": "2023-08-22T06:59:30.346735Z", - "iopub.status.idle": "2023-08-22T06:59:30.349291Z", - "shell.execute_reply": "2023-08-22T06:59:30.349007Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1297,12 +1212,7 @@ "execution_count": 18, "id": "148ed59d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.351073Z", - "iopub.status.busy": "2023-08-22T06:59:30.350934Z", - "iopub.status.idle": "2023-08-22T06:59:30.353516Z", - "shell.execute_reply": "2023-08-22T06:59:30.353225Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1336,12 +1246,7 @@ "execution_count": 19, "id": "d9d7b844", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.355404Z", - "iopub.status.busy": "2023-08-22T06:59:30.355267Z", - "iopub.status.idle": "2023-08-22T06:59:30.357759Z", - "shell.execute_reply": "2023-08-22T06:59:30.357461Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1396,12 +1301,7 @@ "execution_count": 20, "id": "79de2913", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.359458Z", - "iopub.status.busy": "2023-08-22T06:59:30.359349Z", - "iopub.status.idle": "2023-08-22T06:59:30.361244Z", - "shell.execute_reply": "2023-08-22T06:59:30.360996Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1430,12 +1330,7 @@ "execution_count": 21, "id": "a9b843c7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.362847Z", - "iopub.status.busy": "2023-08-22T06:59:30.362727Z", - "iopub.status.idle": "2023-08-22T06:59:30.364665Z", - "shell.execute_reply": "2023-08-22T06:59:30.364410Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1470,12 +1365,7 @@ "execution_count": 22, "id": "7e116800", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.366228Z", - "iopub.status.busy": "2023-08-22T06:59:30.366005Z", - "iopub.status.idle": "2023-08-22T06:59:30.451927Z", - "shell.execute_reply": "2023-08-22T06:59:30.451614Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1534,12 +1424,7 @@ "execution_count": 23, "id": "b524399e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.453712Z", - "iopub.status.busy": "2023-08-22T06:59:30.453582Z", - "iopub.status.idle": "2023-08-22T06:59:30.544088Z", - "shell.execute_reply": "2023-08-22T06:59:30.543647Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1582,12 +1467,7 @@ "execution_count": 24, "id": "8c95b6b0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.545979Z", - "iopub.status.busy": "2023-08-22T06:59:30.545829Z", - "iopub.status.idle": "2023-08-22T06:59:30.629333Z", - "shell.execute_reply": "2023-08-22T06:59:30.628907Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1650,12 +1530,7 @@ "execution_count": 25, "id": "04ed8362", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.631304Z", - "iopub.status.busy": "2023-08-22T06:59:30.631172Z", - "iopub.status.idle": "2023-08-22T06:59:30.643510Z", - "shell.execute_reply": "2023-08-22T06:59:30.643228Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1748,12 +1623,7 @@ "execution_count": 26, "id": "6483e190", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.645260Z", - "iopub.status.busy": "2023-08-22T06:59:30.645134Z", - "iopub.status.idle": "2023-08-22T06:59:30.647637Z", - "shell.execute_reply": "2023-08-22T06:59:30.647310Z" - } + "execution": {} }, "outputs": [ { @@ -1788,12 +1658,7 @@ "execution_count": 27, "id": "99a6f9d0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.649333Z", - "iopub.status.busy": "2023-08-22T06:59:30.649188Z", - "iopub.status.idle": "2023-08-22T06:59:30.667501Z", - "shell.execute_reply": "2023-08-22T06:59:30.667201Z" - } + "execution": {} }, "outputs": [ { @@ -1964,12 +1829,7 @@ "execution_count": 28, "id": "78cf55d0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.669290Z", - "iopub.status.busy": "2023-08-22T06:59:30.669145Z", - "iopub.status.idle": "2023-08-22T06:59:30.687452Z", - "shell.execute_reply": "2023-08-22T06:59:30.687132Z" - } + "execution": {} }, "outputs": [ { @@ -2152,12 +2012,7 @@ "execution_count": 29, "id": "902f6474", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.689227Z", - "iopub.status.busy": "2023-08-22T06:59:30.689072Z", - "iopub.status.idle": "2023-08-22T06:59:30.695976Z", - "shell.execute_reply": "2023-08-22T06:59:30.695604Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2284,12 +2139,7 @@ "execution_count": 30, "id": "ea1c88e9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.697875Z", - "iopub.status.busy": "2023-08-22T06:59:30.697744Z", - "iopub.status.idle": "2023-08-22T06:59:30.702854Z", - "shell.execute_reply": "2023-08-22T06:59:30.702396Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -2317,12 +2167,7 @@ "execution_count": 31, "id": "e9ff159c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.705060Z", - "iopub.status.busy": "2023-08-22T06:59:30.704928Z", - "iopub.status.idle": "2023-08-22T06:59:30.718546Z", - "shell.execute_reply": "2023-08-22T06:59:30.718288Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2425,12 +2270,7 @@ "execution_count": 32, "id": "c98f54b2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.720315Z", - "iopub.status.busy": "2023-08-22T06:59:30.720166Z", - "iopub.status.idle": "2023-08-22T06:59:30.733544Z", - "shell.execute_reply": "2023-08-22T06:59:30.733105Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2543,12 +2383,7 @@ "execution_count": 33, "id": "53065cac", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.735632Z", - "iopub.status.busy": "2023-08-22T06:59:30.735509Z", - "iopub.status.idle": "2023-08-22T06:59:30.740862Z", - "shell.execute_reply": "2023-08-22T06:59:30.740579Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2653,12 +2488,7 @@ "execution_count": 34, "id": "8c654809", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.742508Z", - "iopub.status.busy": "2023-08-22T06:59:30.742387Z", - "iopub.status.idle": "2023-08-22T06:59:30.835769Z", - "shell.execute_reply": "2023-08-22T06:59:30.835368Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2719,12 +2549,7 @@ "execution_count": 35, "id": "2182f0ec", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.837767Z", - "iopub.status.busy": "2023-08-22T06:59:30.837636Z", - "iopub.status.idle": "2023-08-22T06:59:30.842380Z", - "shell.execute_reply": "2023-08-22T06:59:30.842026Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2770,12 +2595,7 @@ "execution_count": 36, "id": "d614fdcb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:30.844518Z", - "iopub.status.busy": "2023-08-22T06:59:30.844367Z", - "iopub.status.idle": "2023-08-22T06:59:30.871405Z", - "shell.execute_reply": "2023-08-22T06:59:30.871052Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ diff --git a/Ch04-classification-lab.ipynb b/Ch04-classification-lab.ipynb index eaa27c9..a4d5e34 100644 --- a/Ch04-classification-lab.ipynb +++ b/Ch04-classification-lab.ipynb @@ -45,12 +45,7 @@ "execution_count": 1, "id": "95d28c33", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:34.104356Z", - "iopub.status.busy": "2023-08-22T06:59:34.104253Z", - "iopub.status.idle": "2023-08-22T06:59:35.054094Z", - "shell.execute_reply": "2023-08-22T06:59:35.053754Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -76,12 +71,7 @@ "execution_count": 2, "id": "f7fb5f2a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.056108Z", - "iopub.status.busy": "2023-08-22T06:59:35.055950Z", - "iopub.status.idle": "2023-08-22T06:59:35.206682Z", - "shell.execute_reply": "2023-08-22T06:59:35.206381Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -111,12 +101,7 @@ "execution_count": 3, "id": "7845390b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.208609Z", - "iopub.status.busy": "2023-08-22T06:59:35.208499Z", - "iopub.status.idle": "2023-08-22T06:59:35.218856Z", - "shell.execute_reply": "2023-08-22T06:59:35.218543Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -331,12 +316,7 @@ "execution_count": 4, "id": "a92e287a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.220822Z", - "iopub.status.busy": "2023-08-22T06:59:35.220658Z", - "iopub.status.idle": "2023-08-22T06:59:35.223146Z", - "shell.execute_reply": "2023-08-22T06:59:35.222864Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -375,12 +355,7 @@ "execution_count": 5, "id": "96bb1e00", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.224965Z", - "iopub.status.busy": "2023-08-22T06:59:35.224831Z", - "iopub.status.idle": "2023-08-22T06:59:35.229701Z", - "shell.execute_reply": "2023-08-22T06:59:35.229442Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -556,12 +531,7 @@ "execution_count": 6, "id": "4ddb96ba", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.231485Z", - "iopub.status.busy": "2023-08-22T06:59:35.231354Z", - "iopub.status.idle": "2023-08-22T06:59:35.319629Z", - "shell.execute_reply": "2023-08-22T06:59:35.319199Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -603,12 +573,7 @@ "execution_count": 7, "id": "df59bcac", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.321733Z", - "iopub.status.busy": "2023-08-22T06:59:35.321597Z", - "iopub.status.idle": "2023-08-22T06:59:35.382350Z", - "shell.execute_reply": "2023-08-22T06:59:35.381919Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -743,12 +708,7 @@ "execution_count": 8, "id": "f45f26de", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.384570Z", - "iopub.status.busy": "2023-08-22T06:59:35.384386Z", - "iopub.status.idle": "2023-08-22T06:59:35.387199Z", - "shell.execute_reply": "2023-08-22T06:59:35.386864Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -788,12 +748,7 @@ "execution_count": 9, "id": "e9f38895", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.388777Z", - "iopub.status.busy": "2023-08-22T06:59:35.388675Z", - "iopub.status.idle": "2023-08-22T06:59:35.391038Z", - "shell.execute_reply": "2023-08-22T06:59:35.390743Z" - } + "execution": {} }, "outputs": [ { @@ -839,12 +794,7 @@ "execution_count": 10, "id": "4f20356d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.392617Z", - "iopub.status.busy": "2023-08-22T06:59:35.392475Z", - "iopub.status.idle": "2023-08-22T06:59:35.395311Z", - "shell.execute_reply": "2023-08-22T06:59:35.395042Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -883,12 +833,7 @@ "execution_count": 11, "id": "152b3063", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.396940Z", - "iopub.status.busy": "2023-08-22T06:59:35.396826Z", - "iopub.status.idle": "2023-08-22T06:59:35.398854Z", - "shell.execute_reply": "2023-08-22T06:59:35.398548Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -916,12 +861,7 @@ "execution_count": 12, "id": "0f89f7ae", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.400476Z", - "iopub.status.busy": "2023-08-22T06:59:35.400370Z", - "iopub.status.idle": "2023-08-22T06:59:35.406304Z", - "shell.execute_reply": "2023-08-22T06:59:35.405994Z" - } + "execution": {} }, "outputs": [ { @@ -1005,12 +945,7 @@ "execution_count": 13, "id": "d55dd7ec", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.408001Z", - "iopub.status.busy": "2023-08-22T06:59:35.407888Z", - "iopub.status.idle": "2023-08-22T06:59:35.410520Z", - "shell.execute_reply": "2023-08-22T06:59:35.410222Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1068,12 +1003,7 @@ "execution_count": 14, "id": "b998a060", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.412348Z", - "iopub.status.busy": "2023-08-22T06:59:35.412200Z", - "iopub.status.idle": "2023-08-22T06:59:35.415670Z", - "shell.execute_reply": "2023-08-22T06:59:35.415360Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1134,12 +1064,7 @@ "execution_count": 15, "id": "814e34ce", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.417692Z", - "iopub.status.busy": "2023-08-22T06:59:35.417600Z", - "iopub.status.idle": "2023-08-22T06:59:35.422315Z", - "shell.execute_reply": "2023-08-22T06:59:35.421995Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1171,12 +1096,7 @@ "execution_count": 16, "id": "644823f9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.424241Z", - "iopub.status.busy": "2023-08-22T06:59:35.424099Z", - "iopub.status.idle": "2023-08-22T06:59:35.426371Z", - "shell.execute_reply": "2023-08-22T06:59:35.425999Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1200,12 +1120,7 @@ "execution_count": 17, "id": "51217c85", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.428375Z", - "iopub.status.busy": "2023-08-22T06:59:35.428239Z", - "iopub.status.idle": "2023-08-22T06:59:35.432849Z", - "shell.execute_reply": "2023-08-22T06:59:35.432491Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1285,12 +1200,7 @@ "execution_count": 18, "id": "a73446bf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.434489Z", - "iopub.status.busy": "2023-08-22T06:59:35.434371Z", - "iopub.status.idle": "2023-08-22T06:59:35.437237Z", - "shell.execute_reply": "2023-08-22T06:59:35.436914Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1343,12 +1253,7 @@ "execution_count": 19, "id": "8174767d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.439129Z", - "iopub.status.busy": "2023-08-22T06:59:35.438980Z", - "iopub.status.idle": "2023-08-22T06:59:35.449036Z", - "shell.execute_reply": "2023-08-22T06:59:35.448757Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1437,12 +1342,7 @@ "execution_count": 20, "id": "cba7e815", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.450840Z", - "iopub.status.busy": "2023-08-22T06:59:35.450725Z", - "iopub.status.idle": "2023-08-22T06:59:35.452944Z", - "shell.execute_reply": "2023-08-22T06:59:35.452674Z" - } + "execution": {} }, "outputs": [ { @@ -1491,12 +1391,7 @@ "execution_count": 21, "id": "97993185", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.454539Z", - "iopub.status.busy": "2023-08-22T06:59:35.454423Z", - "iopub.status.idle": "2023-08-22T06:59:35.458834Z", - "shell.execute_reply": "2023-08-22T06:59:35.458553Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1543,12 +1438,7 @@ "execution_count": 22, "id": "4bc774e9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.460658Z", - "iopub.status.busy": "2023-08-22T06:59:35.460534Z", - "iopub.status.idle": "2023-08-22T06:59:35.462216Z", - "shell.execute_reply": "2023-08-22T06:59:35.461957Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1571,12 +1461,7 @@ "execution_count": 23, "id": "8c6c0723", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.463858Z", - "iopub.status.busy": "2023-08-22T06:59:35.463759Z", - "iopub.status.idle": "2023-08-22T06:59:35.469987Z", - "shell.execute_reply": "2023-08-22T06:59:35.469710Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1627,12 +1512,7 @@ "execution_count": 24, "id": "cf8fd5ac", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.471584Z", - "iopub.status.busy": "2023-08-22T06:59:35.471470Z", - "iopub.status.idle": "2023-08-22T06:59:35.473834Z", - "shell.execute_reply": "2023-08-22T06:59:35.473552Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1668,12 +1548,7 @@ "execution_count": 25, "id": "bfd6b3f8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.475293Z", - "iopub.status.busy": "2023-08-22T06:59:35.475189Z", - "iopub.status.idle": "2023-08-22T06:59:35.477448Z", - "shell.execute_reply": "2023-08-22T06:59:35.477007Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1706,12 +1581,7 @@ "execution_count": 26, "id": "a1f75de4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.479354Z", - "iopub.status.busy": "2023-08-22T06:59:35.479219Z", - "iopub.status.idle": "2023-08-22T06:59:35.481609Z", - "shell.execute_reply": "2023-08-22T06:59:35.481305Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1743,12 +1613,7 @@ "execution_count": 27, "id": "82794178", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.483264Z", - "iopub.status.busy": "2023-08-22T06:59:35.483156Z", - "iopub.status.idle": "2023-08-22T06:59:35.485247Z", - "shell.execute_reply": "2023-08-22T06:59:35.484948Z" - } + "execution": {} }, "outputs": [ { @@ -1781,12 +1646,7 @@ "execution_count": 28, "id": "9b571047", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.486831Z", - "iopub.status.busy": "2023-08-22T06:59:35.486719Z", - "iopub.status.idle": "2023-08-22T06:59:35.489231Z", - "shell.execute_reply": "2023-08-22T06:59:35.488879Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1808,12 +1668,7 @@ "execution_count": 29, "id": "60f3d13a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.490888Z", - "iopub.status.busy": "2023-08-22T06:59:35.490781Z", - "iopub.status.idle": "2023-08-22T06:59:35.494972Z", - "shell.execute_reply": "2023-08-22T06:59:35.494699Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1895,12 +1750,7 @@ "execution_count": 30, "id": "96e680d9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.496550Z", - "iopub.status.busy": "2023-08-22T06:59:35.496460Z", - "iopub.status.idle": "2023-08-22T06:59:35.500050Z", - "shell.execute_reply": "2023-08-22T06:59:35.499652Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1939,12 +1789,7 @@ "execution_count": 31, "id": "b6695125", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.501821Z", - "iopub.status.busy": "2023-08-22T06:59:35.501696Z", - "iopub.status.idle": "2023-08-22T06:59:35.504118Z", - "shell.execute_reply": "2023-08-22T06:59:35.503779Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1985,12 +1830,7 @@ "execution_count": 32, "id": "3f38a14e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.505774Z", - "iopub.status.busy": "2023-08-22T06:59:35.505669Z", - "iopub.status.idle": "2023-08-22T06:59:35.508018Z", - "shell.execute_reply": "2023-08-22T06:59:35.507737Z" - } + "execution": {} }, "outputs": [ { @@ -2050,12 +1890,7 @@ "execution_count": 33, "id": "9b645803", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.510093Z", - "iopub.status.busy": "2023-08-22T06:59:35.509967Z", - "iopub.status.idle": "2023-08-22T06:59:35.514060Z", - "shell.execute_reply": "2023-08-22T06:59:35.513746Z" - } + "execution": {} }, "outputs": [ { @@ -2090,12 +1925,7 @@ "execution_count": 34, "id": "abfae544", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.515774Z", - "iopub.status.busy": "2023-08-22T06:59:35.515682Z", - "iopub.status.idle": "2023-08-22T06:59:35.518169Z", - "shell.execute_reply": "2023-08-22T06:59:35.517903Z" - } + "execution": {} }, "outputs": [ { @@ -2129,12 +1959,7 @@ "execution_count": 35, "id": "2a3bb41e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.519953Z", - "iopub.status.busy": "2023-08-22T06:59:35.519849Z", - "iopub.status.idle": "2023-08-22T06:59:35.522248Z", - "shell.execute_reply": "2023-08-22T06:59:35.521930Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2171,12 +1996,7 @@ "execution_count": 36, "id": "1c64310b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.523826Z", - "iopub.status.busy": "2023-08-22T06:59:35.523714Z", - "iopub.status.idle": "2023-08-22T06:59:35.528214Z", - "shell.execute_reply": "2023-08-22T06:59:35.527959Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2256,12 +2076,7 @@ "execution_count": 37, "id": "0c05c5a8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.530152Z", - "iopub.status.busy": "2023-08-22T06:59:35.530024Z", - "iopub.status.idle": "2023-08-22T06:59:35.532713Z", - "shell.execute_reply": "2023-08-22T06:59:35.532375Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2312,12 +2127,7 @@ "execution_count": 38, "id": "47d85305", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.534353Z", - "iopub.status.busy": "2023-08-22T06:59:35.534232Z", - "iopub.status.idle": "2023-08-22T06:59:35.537918Z", - "shell.execute_reply": "2023-08-22T06:59:35.537612Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2353,12 +2163,7 @@ "execution_count": 39, "id": "c553aadf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.539480Z", - "iopub.status.busy": "2023-08-22T06:59:35.539391Z", - "iopub.status.idle": "2023-08-22T06:59:35.541924Z", - "shell.execute_reply": "2023-08-22T06:59:35.541560Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2390,12 +2195,7 @@ "execution_count": 40, "id": "4604bd3d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.543604Z", - "iopub.status.busy": "2023-08-22T06:59:35.543514Z", - "iopub.status.idle": "2023-08-22T06:59:35.545770Z", - "shell.execute_reply": "2023-08-22T06:59:35.545483Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2429,12 +2229,7 @@ "execution_count": 41, "id": "5ac2cabe", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.547333Z", - "iopub.status.busy": "2023-08-22T06:59:35.547227Z", - "iopub.status.idle": "2023-08-22T06:59:35.549381Z", - "shell.execute_reply": "2023-08-22T06:59:35.549124Z" - } + "execution": {} }, "outputs": [ { @@ -2466,12 +2261,7 @@ "execution_count": 42, "id": "f8623945", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.551288Z", - "iopub.status.busy": "2023-08-22T06:59:35.551134Z", - "iopub.status.idle": "2023-08-22T06:59:35.553370Z", - "shell.execute_reply": "2023-08-22T06:59:35.553102Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2506,12 +2296,7 @@ "execution_count": 43, "id": "0790f26e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.554980Z", - "iopub.status.busy": "2023-08-22T06:59:35.554846Z", - "iopub.status.idle": "2023-08-22T06:59:35.557806Z", - "shell.execute_reply": "2023-08-22T06:59:35.557573Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2545,12 +2330,7 @@ "execution_count": 44, "id": "4a8cf0ce", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.559235Z", - "iopub.status.busy": "2023-08-22T06:59:35.559122Z", - "iopub.status.idle": "2023-08-22T06:59:35.562452Z", - "shell.execute_reply": "2023-08-22T06:59:35.562071Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2585,12 +2365,7 @@ "execution_count": 45, "id": "94e7ff1a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.564237Z", - "iopub.status.busy": "2023-08-22T06:59:35.564138Z", - "iopub.status.idle": "2023-08-22T06:59:35.568635Z", - "shell.execute_reply": "2023-08-22T06:59:35.568359Z" - } + "execution": {} }, "outputs": [ { @@ -2670,12 +2445,7 @@ "execution_count": 46, "id": "137e23aa", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.570127Z", - "iopub.status.busy": "2023-08-22T06:59:35.570011Z", - "iopub.status.idle": "2023-08-22T06:59:35.573407Z", - "shell.execute_reply": "2023-08-22T06:59:35.573097Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2720,12 +2490,7 @@ "execution_count": 47, "id": "142c5217", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.575043Z", - "iopub.status.busy": "2023-08-22T06:59:35.574949Z", - "iopub.status.idle": "2023-08-22T06:59:35.585246Z", - "shell.execute_reply": "2023-08-22T06:59:35.584962Z" - } + "execution": {} }, "outputs": [ { @@ -2808,12 +2573,7 @@ "execution_count": 48, "id": "f5a272ee", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.587100Z", - "iopub.status.busy": "2023-08-22T06:59:35.586985Z", - "iopub.status.idle": "2023-08-22T06:59:35.589510Z", - "shell.execute_reply": "2023-08-22T06:59:35.589151Z" - } + "execution": {} }, "outputs": [ { @@ -2845,12 +2605,7 @@ "execution_count": 49, "id": "95e206a8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.591165Z", - "iopub.status.busy": "2023-08-22T06:59:35.591045Z", - "iopub.status.idle": "2023-08-22T06:59:35.598648Z", - "shell.execute_reply": "2023-08-22T06:59:35.598383Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2900,12 +2655,7 @@ "execution_count": 50, "id": "422563b7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.600269Z", - "iopub.status.busy": "2023-08-22T06:59:35.600186Z", - "iopub.status.idle": "2023-08-22T06:59:35.616715Z", - "shell.execute_reply": "2023-08-22T06:59:35.616375Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2944,12 +2694,7 @@ "execution_count": 51, "id": "583c860c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.618509Z", - "iopub.status.busy": "2023-08-22T06:59:35.618392Z", - "iopub.status.idle": "2023-08-22T06:59:35.620427Z", - "shell.execute_reply": "2023-08-22T06:59:35.620213Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2981,12 +2726,7 @@ "execution_count": 52, "id": "19ee3bf2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.622075Z", - "iopub.status.busy": "2023-08-22T06:59:35.621970Z", - "iopub.status.idle": "2023-08-22T06:59:35.624662Z", - "shell.execute_reply": "2023-08-22T06:59:35.624306Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -3030,12 +2770,7 @@ "execution_count": 53, "id": "fdc0e5f1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.626535Z", - "iopub.status.busy": "2023-08-22T06:59:35.626411Z", - "iopub.status.idle": "2023-08-22T06:59:35.628326Z", - "shell.execute_reply": "2023-08-22T06:59:35.628023Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3069,12 +2804,7 @@ "execution_count": 54, "id": "2bb9d48b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.630001Z", - "iopub.status.busy": "2023-08-22T06:59:35.629870Z", - "iopub.status.idle": "2023-08-22T06:59:35.636000Z", - "shell.execute_reply": "2023-08-22T06:59:35.635697Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3097,12 +2827,7 @@ "execution_count": 55, "id": "649b57b3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.637746Z", - "iopub.status.busy": "2023-08-22T06:59:35.637618Z", - "iopub.status.idle": "2023-08-22T06:59:35.643400Z", - "shell.execute_reply": "2023-08-22T06:59:35.643102Z" - } + "execution": {} }, "outputs": [ { @@ -3153,12 +2878,7 @@ "execution_count": 56, "id": "d0aafd5e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.644912Z", - "iopub.status.busy": "2023-08-22T06:59:35.644814Z", - "iopub.status.idle": "2023-08-22T06:59:35.648144Z", - "shell.execute_reply": "2023-08-22T06:59:35.647828Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3188,12 +2908,7 @@ "execution_count": 57, "id": "ad02fb42", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.649991Z", - "iopub.status.busy": "2023-08-22T06:59:35.649857Z", - "iopub.status.idle": "2023-08-22T06:59:35.851869Z", - "shell.execute_reply": "2023-08-22T06:59:35.850800Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3241,12 +2956,7 @@ "execution_count": 58, "id": "901f772e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.857360Z", - "iopub.status.busy": "2023-08-22T06:59:35.856994Z", - "iopub.status.idle": "2023-08-22T06:59:35.868149Z", - "shell.execute_reply": "2023-08-22T06:59:35.867281Z" - } + "execution": {} }, "outputs": [ { @@ -3326,12 +3036,7 @@ "execution_count": 59, "id": "684f8941", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.872302Z", - "iopub.status.busy": "2023-08-22T06:59:35.871992Z", - "iopub.status.idle": "2023-08-22T06:59:35.881501Z", - "shell.execute_reply": "2023-08-22T06:59:35.879490Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3370,12 +3075,7 @@ "execution_count": 60, "id": "4d984cf0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:35.885276Z", - "iopub.status.busy": "2023-08-22T06:59:35.884561Z", - "iopub.status.idle": "2023-08-22T06:59:36.032887Z", - "shell.execute_reply": "2023-08-22T06:59:36.032527Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3437,12 +3137,7 @@ "execution_count": 61, "id": "d24f4e50", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.035053Z", - "iopub.status.busy": "2023-08-22T06:59:36.034905Z", - "iopub.status.idle": "2023-08-22T06:59:36.559516Z", - "shell.execute_reply": "2023-08-22T06:59:36.557112Z" - } + "execution": {} }, "outputs": [ { @@ -3534,12 +3229,7 @@ "execution_count": 62, "id": "25152580", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.564551Z", - "iopub.status.busy": "2023-08-22T06:59:36.564196Z", - "iopub.status.idle": "2023-08-22T06:59:36.582460Z", - "shell.execute_reply": "2023-08-22T06:59:36.580635Z" - } + "execution": {} }, "outputs": [ { @@ -3609,12 +3299,7 @@ "execution_count": 63, "id": "b8ea6e08", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.590532Z", - "iopub.status.busy": "2023-08-22T06:59:36.590038Z", - "iopub.status.idle": "2023-08-22T06:59:36.605488Z", - "shell.execute_reply": "2023-08-22T06:59:36.602880Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3649,12 +3334,7 @@ "execution_count": 64, "id": "def80d79", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.610362Z", - "iopub.status.busy": "2023-08-22T06:59:36.609709Z", - "iopub.status.idle": "2023-08-22T06:59:36.624089Z", - "shell.execute_reply": "2023-08-22T06:59:36.623200Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3675,12 +3355,7 @@ "execution_count": 65, "id": "f899d5ab", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.630386Z", - "iopub.status.busy": "2023-08-22T06:59:36.629966Z", - "iopub.status.idle": "2023-08-22T06:59:36.636403Z", - "shell.execute_reply": "2023-08-22T06:59:36.635734Z" - } + "execution": {} }, "outputs": [ { @@ -3717,12 +3392,7 @@ "execution_count": 66, "id": "76f4cea5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.642541Z", - "iopub.status.busy": "2023-08-22T06:59:36.642195Z", - "iopub.status.idle": "2023-08-22T06:59:36.742213Z", - "shell.execute_reply": "2023-08-22T06:59:36.740543Z" - } + "execution": {} }, "outputs": [ { @@ -4121,12 +3791,7 @@ "execution_count": 67, "id": "5778ada8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.748318Z", - "iopub.status.busy": "2023-08-22T06:59:36.747980Z", - "iopub.status.idle": "2023-08-22T06:59:36.753044Z", - "shell.execute_reply": "2023-08-22T06:59:36.752001Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -4148,12 +3813,7 @@ "execution_count": 68, "id": "c6da14b9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.761294Z", - "iopub.status.busy": "2023-08-22T06:59:36.760741Z", - "iopub.status.idle": "2023-08-22T06:59:36.824675Z", - "shell.execute_reply": "2023-08-22T06:59:36.823419Z" - } + "execution": {} }, "outputs": [ { @@ -4558,12 +4218,7 @@ "execution_count": 69, "id": "461d57c5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.832613Z", - "iopub.status.busy": "2023-08-22T06:59:36.831243Z", - "iopub.status.idle": "2023-08-22T06:59:36.839557Z", - "shell.execute_reply": "2023-08-22T06:59:36.838190Z" - } + "execution": {} }, "outputs": [ { @@ -4595,12 +4250,7 @@ "execution_count": 70, "id": "05d33247", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.846635Z", - "iopub.status.busy": "2023-08-22T06:59:36.846336Z", - "iopub.status.idle": "2023-08-22T06:59:36.854114Z", - "shell.execute_reply": "2023-08-22T06:59:36.853108Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -4638,12 +4288,7 @@ "execution_count": 71, "id": "bee42b38", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.861230Z", - "iopub.status.busy": "2023-08-22T06:59:36.860269Z", - "iopub.status.idle": "2023-08-22T06:59:36.869236Z", - "shell.execute_reply": "2023-08-22T06:59:36.868250Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -4687,12 +4332,7 @@ "execution_count": 72, "id": "4aa60857", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.876998Z", - "iopub.status.busy": "2023-08-22T06:59:36.876016Z", - "iopub.status.idle": "2023-08-22T06:59:36.883621Z", - "shell.execute_reply": "2023-08-22T06:59:36.882372Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -4744,12 +4384,7 @@ "execution_count": 73, "id": "894d3e2c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:36.889441Z", - "iopub.status.busy": "2023-08-22T06:59:36.888780Z", - "iopub.status.idle": "2023-08-22T06:59:37.004601Z", - "shell.execute_reply": "2023-08-22T06:59:37.003102Z" - } + "execution": {} }, "outputs": [ { @@ -4786,12 +4421,7 @@ "execution_count": 74, "id": "d636746e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:37.014352Z", - "iopub.status.busy": "2023-08-22T06:59:37.013713Z", - "iopub.status.idle": "2023-08-22T06:59:37.022133Z", - "shell.execute_reply": "2023-08-22T06:59:37.021189Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -4815,12 +4445,7 @@ "execution_count": 75, "id": "ce6a1623", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:37.030881Z", - "iopub.status.busy": "2023-08-22T06:59:37.030334Z", - "iopub.status.idle": "2023-08-22T06:59:37.126992Z", - "shell.execute_reply": "2023-08-22T06:59:37.126566Z" - } + "execution": {} }, "outputs": [ { @@ -4861,12 +4486,7 @@ "execution_count": 76, "id": "9fb8b759", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:37.128893Z", - "iopub.status.busy": "2023-08-22T06:59:37.128737Z", - "iopub.status.idle": "2023-08-22T06:59:37.220758Z", - "shell.execute_reply": "2023-08-22T06:59:37.219419Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -4886,12 +4506,7 @@ "execution_count": 77, "id": "ee272341", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:37.227931Z", - "iopub.status.busy": "2023-08-22T06:59:37.227304Z", - "iopub.status.idle": "2023-08-22T06:59:37.252847Z", - "shell.execute_reply": "2023-08-22T06:59:37.251756Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -4920,12 +4535,7 @@ "execution_count": 78, "id": "1f5bde07", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:37.263402Z", - "iopub.status.busy": "2023-08-22T06:59:37.262273Z", - "iopub.status.idle": "2023-08-22T06:59:37.482693Z", - "shell.execute_reply": "2023-08-22T06:59:37.479472Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -4977,12 +4587,7 @@ "execution_count": 79, "id": "b0bd66a1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:37.487973Z", - "iopub.status.busy": "2023-08-22T06:59:37.486725Z", - "iopub.status.idle": "2023-08-22T06:59:37.588898Z", - "shell.execute_reply": "2023-08-22T06:59:37.588557Z" - } + "execution": {} }, "outputs": [ { diff --git a/Ch05-resample-lab.ipynb b/Ch05-resample-lab.ipynb index 2314c64..4edfa41 100644 --- a/Ch05-resample-lab.ipynb +++ b/Ch05-resample-lab.ipynb @@ -28,12 +28,7 @@ "execution_count": 1, "id": "e7712cfe", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:40.827828Z", - "iopub.status.busy": "2023-08-22T06:59:40.827725Z", - "iopub.status.idle": "2023-08-22T06:59:41.658013Z", - "shell.execute_reply": "2023-08-22T06:59:41.657645Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -60,12 +55,7 @@ "execution_count": 2, "id": "21c2ed4f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.659951Z", - "iopub.status.busy": "2023-08-22T06:59:41.659798Z", - "iopub.status.idle": "2023-08-22T06:59:41.661744Z", - "shell.execute_reply": "2023-08-22T06:59:41.661437Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -104,12 +94,7 @@ "execution_count": 3, "id": "8af59641", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.663216Z", - "iopub.status.busy": "2023-08-22T06:59:41.663117Z", - "iopub.status.idle": "2023-08-22T06:59:41.667680Z", - "shell.execute_reply": "2023-08-22T06:59:41.667343Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -132,12 +117,7 @@ "execution_count": 4, "id": "d9b0b7c8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.669501Z", - "iopub.status.busy": "2023-08-22T06:59:41.669395Z", - "iopub.status.idle": "2023-08-22T06:59:41.674461Z", - "shell.execute_reply": "2023-08-22T06:59:41.674176Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -162,12 +142,7 @@ "execution_count": 5, "id": "3e77d831", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.676257Z", - "iopub.status.busy": "2023-08-22T06:59:41.676134Z", - "iopub.status.idle": "2023-08-22T06:59:41.680728Z", - "shell.execute_reply": "2023-08-22T06:59:41.680456Z" - } + "execution": {} }, "outputs": [ { @@ -206,12 +181,7 @@ "execution_count": 6, "id": "0aa4bfcc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.682418Z", - "iopub.status.busy": "2023-08-22T06:59:41.682304Z", - "iopub.status.idle": "2023-08-22T06:59:41.684531Z", - "shell.execute_reply": "2023-08-22T06:59:41.684276Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -249,12 +219,7 @@ "execution_count": 7, "id": "a0dbd55f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.686084Z", - "iopub.status.busy": "2023-08-22T06:59:41.685977Z", - "iopub.status.idle": "2023-08-22T06:59:41.701551Z", - "shell.execute_reply": "2023-08-22T06:59:41.701265Z" - } + "execution": {} }, "outputs": [ { @@ -293,12 +258,7 @@ "execution_count": 8, "id": "885136a4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.703198Z", - "iopub.status.busy": "2023-08-22T06:59:41.703080Z", - "iopub.status.idle": "2023-08-22T06:59:41.719142Z", - "shell.execute_reply": "2023-08-22T06:59:41.718778Z" - } + "execution": {} }, "outputs": [ { @@ -376,12 +336,7 @@ "execution_count": 9, "id": "6d957d8c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:41.721067Z", - "iopub.status.busy": "2023-08-22T06:59:41.720935Z", - "iopub.status.idle": "2023-08-22T06:59:42.841536Z", - "shell.execute_reply": "2023-08-22T06:59:42.841242Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -444,12 +399,7 @@ "execution_count": 10, "id": "e2b5ce95", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:42.843438Z", - "iopub.status.busy": "2023-08-22T06:59:42.843308Z", - "iopub.status.idle": "2023-08-22T06:59:43.408832Z", - "shell.execute_reply": "2023-08-22T06:59:43.408347Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -501,12 +451,7 @@ "execution_count": 11, "id": "1dda1bd7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.411083Z", - "iopub.status.busy": "2023-08-22T06:59:43.410940Z", - "iopub.status.idle": "2023-08-22T06:59:43.413889Z", - "shell.execute_reply": "2023-08-22T06:59:43.413456Z" - } + "execution": {} }, "outputs": [ { @@ -543,12 +488,7 @@ "execution_count": 12, "id": "fb25fa70", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.415865Z", - "iopub.status.busy": "2023-08-22T06:59:43.415724Z", - "iopub.status.idle": "2023-08-22T06:59:43.437716Z", - "shell.execute_reply": "2023-08-22T06:59:43.437344Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -608,12 +548,7 @@ "execution_count": 13, "id": "d78795cd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.439940Z", - "iopub.status.busy": "2023-08-22T06:59:43.439783Z", - "iopub.status.idle": "2023-08-22T06:59:43.447580Z", - "shell.execute_reply": "2023-08-22T06:59:43.447286Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -652,12 +587,7 @@ "execution_count": 14, "id": "0407ad56", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.449591Z", - "iopub.status.busy": "2023-08-22T06:59:43.449452Z", - "iopub.status.idle": "2023-08-22T06:59:43.483638Z", - "shell.execute_reply": "2023-08-22T06:59:43.483222Z" - } + "execution": {} }, "outputs": [ { @@ -726,12 +656,7 @@ "execution_count": 15, "id": "f04f15bd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.485600Z", - "iopub.status.busy": "2023-08-22T06:59:43.485469Z", - "iopub.status.idle": "2023-08-22T06:59:43.488993Z", - "shell.execute_reply": "2023-08-22T06:59:43.488698Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -760,12 +685,7 @@ "execution_count": 16, "id": "f98c0323", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.490952Z", - "iopub.status.busy": "2023-08-22T06:59:43.490857Z", - "iopub.status.idle": "2023-08-22T06:59:43.494027Z", - "shell.execute_reply": "2023-08-22T06:59:43.493695Z" - } + "execution": {} }, "outputs": [ { @@ -799,12 +719,7 @@ "execution_count": 17, "id": "bcd40175", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.495852Z", - "iopub.status.busy": "2023-08-22T06:59:43.495690Z", - "iopub.status.idle": "2023-08-22T06:59:43.498806Z", - "shell.execute_reply": "2023-08-22T06:59:43.498500Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -842,12 +757,7 @@ "execution_count": 18, "id": "ab6602cd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.500527Z", - "iopub.status.busy": "2023-08-22T06:59:43.500424Z", - "iopub.status.idle": "2023-08-22T06:59:43.502886Z", - "shell.execute_reply": "2023-08-22T06:59:43.502621Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -887,12 +797,7 @@ "execution_count": 19, "id": "4a323513", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.504576Z", - "iopub.status.busy": "2023-08-22T06:59:43.504471Z", - "iopub.status.idle": "2023-08-22T06:59:43.776972Z", - "shell.execute_reply": "2023-08-22T06:59:43.776638Z" - } + "execution": {} }, "outputs": [ { @@ -953,12 +858,7 @@ "execution_count": 20, "id": "0220f3af", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.778737Z", - "iopub.status.busy": "2023-08-22T06:59:43.778622Z", - "iopub.status.idle": "2023-08-22T06:59:43.780660Z", - "shell.execute_reply": "2023-08-22T06:59:43.780373Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -988,12 +888,7 @@ "execution_count": 21, "id": "62037dcb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.782304Z", - "iopub.status.busy": "2023-08-22T06:59:43.782178Z", - "iopub.status.idle": "2023-08-22T06:59:43.783926Z", - "shell.execute_reply": "2023-08-22T06:59:43.783681Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1021,12 +916,7 @@ "execution_count": 22, "id": "b8bdb7a4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.785584Z", - "iopub.status.busy": "2023-08-22T06:59:43.785479Z", - "iopub.status.idle": "2023-08-22T06:59:43.814180Z", - "shell.execute_reply": "2023-08-22T06:59:43.813871Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1072,12 +962,7 @@ "execution_count": 23, "id": "36808258", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:43.815884Z", - "iopub.status.busy": "2023-08-22T06:59:43.815750Z", - "iopub.status.idle": "2023-08-22T06:59:46.317022Z", - "shell.execute_reply": "2023-08-22T06:59:46.316748Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1122,12 +1007,7 @@ "execution_count": 24, "id": "c9aea297", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:46.318852Z", - "iopub.status.busy": "2023-08-22T06:59:46.318744Z", - "iopub.status.idle": "2023-08-22T06:59:46.401268Z", - "shell.execute_reply": "2023-08-22T06:59:46.400928Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1194,12 +1074,7 @@ "execution_count": 25, "id": "79c56529", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:46.403009Z", - "iopub.status.busy": "2023-08-22T06:59:46.402852Z", - "iopub.status.idle": "2023-08-22T06:59:50.039894Z", - "shell.execute_reply": "2023-08-22T06:59:50.039566Z" - } + "execution": {} }, "outputs": [ { @@ -1237,12 +1112,7 @@ "execution_count": 26, "id": "4d0b4edc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:50.041629Z", - "iopub.status.busy": "2023-08-22T06:59:50.041514Z", - "iopub.status.idle": "2023-08-22T06:59:50.052046Z", - "shell.execute_reply": "2023-08-22T06:59:50.051690Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ diff --git a/Ch06-varselect-lab.ipynb b/Ch06-varselect-lab.ipynb index 94ddd1b..4bf0246 100644 --- a/Ch06-varselect-lab.ipynb +++ b/Ch06-varselect-lab.ipynb @@ -25,12 +25,7 @@ "execution_count": 1, "id": "638bdae9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:53.093245Z", - "iopub.status.busy": "2023-08-22T06:59:53.093143Z", - "iopub.status.idle": "2023-08-22T06:59:54.054493Z", - "shell.execute_reply": "2023-08-22T06:59:54.054110Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -60,12 +55,7 @@ "execution_count": 2, "id": "c4b3398b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:54.056594Z", - "iopub.status.busy": "2023-08-22T06:59:54.056412Z", - "iopub.status.idle": "2023-08-22T06:59:56.004484Z", - "shell.execute_reply": "2023-08-22T06:59:56.004082Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -124,12 +114,7 @@ "execution_count": 3, "id": "18d03122", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:56.006401Z", - "iopub.status.busy": "2023-08-22T06:59:56.006276Z", - "iopub.status.idle": "2023-08-22T06:59:56.013280Z", - "shell.execute_reply": "2023-08-22T06:59:56.012957Z" - } + "execution": {} }, "outputs": [ { @@ -163,12 +148,7 @@ "execution_count": 4, "id": "87a4ba00", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:56.014992Z", - "iopub.status.busy": "2023-08-22T06:59:56.014883Z", - "iopub.status.idle": "2023-08-22T06:59:56.017852Z", - "shell.execute_reply": "2023-08-22T06:59:56.017594Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -204,12 +184,7 @@ "execution_count": 5, "id": "97d6b69c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:56.019330Z", - "iopub.status.busy": "2023-08-22T06:59:56.019227Z", - "iopub.status.idle": "2023-08-22T06:59:56.021261Z", - "shell.execute_reply": "2023-08-22T06:59:56.020923Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -236,12 +211,7 @@ "execution_count": 6, "id": "2575e116", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:56.023126Z", - "iopub.status.busy": "2023-08-22T06:59:56.023002Z", - "iopub.status.idle": "2023-08-22T06:59:56.043909Z", - "shell.execute_reply": "2023-08-22T06:59:56.043598Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -264,12 +234,7 @@ "execution_count": 7, "id": "4cfae1c0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:56.046001Z", - "iopub.status.busy": "2023-08-22T06:59:56.045882Z", - "iopub.status.idle": "2023-08-22T06:59:56.047752Z", - "shell.execute_reply": "2023-08-22T06:59:56.047459Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -302,12 +267,7 @@ "execution_count": 8, "id": "98b62676", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:56.049394Z", - "iopub.status.busy": "2023-08-22T06:59:56.049284Z", - "iopub.status.idle": "2023-08-22T06:59:56.051180Z", - "shell.execute_reply": "2023-08-22T06:59:56.050841Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -333,12 +293,7 @@ "execution_count": 9, "id": "3d1bd25a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:56.053484Z", - "iopub.status.busy": "2023-08-22T06:59:56.053197Z", - "iopub.status.idle": "2023-08-22T06:59:57.116576Z", - "shell.execute_reply": "2023-08-22T06:59:57.116202Z" - } + "execution": {} }, "outputs": [ { @@ -390,12 +345,7 @@ "execution_count": 10, "id": "b25dfa6c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:57.118521Z", - "iopub.status.busy": "2023-08-22T06:59:57.118381Z", - "iopub.status.idle": "2023-08-22T06:59:57.757333Z", - "shell.execute_reply": "2023-08-22T06:59:57.756933Z" - } + "execution": {} }, "outputs": [ { @@ -456,12 +406,7 @@ "execution_count": 11, "id": "0ef3f82d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:57.759379Z", - "iopub.status.busy": "2023-08-22T06:59:57.759234Z", - "iopub.status.idle": "2023-08-22T06:59:57.761149Z", - "shell.execute_reply": "2023-08-22T06:59:57.760820Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -484,12 +429,7 @@ "execution_count": 12, "id": "d4a8cf16", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:57.762826Z", - "iopub.status.busy": "2023-08-22T06:59:57.762724Z", - "iopub.status.idle": "2023-08-22T06:59:58.384792Z", - "shell.execute_reply": "2023-08-22T06:59:58.384441Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -529,12 +469,7 @@ "execution_count": 13, "id": "ce40a9ba", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:58.386657Z", - "iopub.status.busy": "2023-08-22T06:59:58.386521Z", - "iopub.status.idle": "2023-08-22T06:59:58.504180Z", - "shell.execute_reply": "2023-08-22T06:59:58.503223Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -598,12 +533,7 @@ "execution_count": 14, "id": "c7416ff6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:59:58.509147Z", - "iopub.status.busy": "2023-08-22T06:59:58.508897Z", - "iopub.status.idle": "2023-08-22T07:00:01.677521Z", - "shell.execute_reply": "2023-08-22T07:00:01.677198Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -652,12 +582,7 @@ "execution_count": 15, "id": "a2ae089b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:01.679228Z", - "iopub.status.busy": "2023-08-22T07:00:01.679111Z", - "iopub.status.idle": "2023-08-22T07:00:01.681995Z", - "shell.execute_reply": "2023-08-22T07:00:01.681709Z" - } + "execution": {} }, "outputs": [ { @@ -694,12 +619,7 @@ "execution_count": 16, "id": "1681a9db", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:01.683590Z", - "iopub.status.busy": "2023-08-22T07:00:01.683477Z", - "iopub.status.idle": "2023-08-22T07:00:01.759136Z", - "shell.execute_reply": "2023-08-22T07:00:01.758804Z" - } + "execution": {} }, "outputs": [ { @@ -740,12 +660,7 @@ "execution_count": 17, "id": "5764b0ba", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:01.760831Z", - "iopub.status.busy": "2023-08-22T07:00:01.760716Z", - "iopub.status.idle": "2023-08-22T07:00:02.384829Z", - "shell.execute_reply": "2023-08-22T07:00:02.384507Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -774,12 +689,7 @@ "execution_count": 18, "id": "d76f3d3a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:02.387276Z", - "iopub.status.busy": "2023-08-22T07:00:02.386879Z", - "iopub.status.idle": "2023-08-22T07:00:02.468255Z", - "shell.execute_reply": "2023-08-22T07:00:02.467684Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -827,12 +737,7 @@ "execution_count": 19, "id": "ed25c2d4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:02.470331Z", - "iopub.status.busy": "2023-08-22T07:00:02.470178Z", - "iopub.status.idle": "2023-08-22T07:00:02.491378Z", - "shell.execute_reply": "2023-08-22T07:00:02.491048Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -856,12 +761,7 @@ "execution_count": 20, "id": "31e99e02", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:02.493549Z", - "iopub.status.busy": "2023-08-22T07:00:02.493371Z", - "iopub.status.idle": "2023-08-22T07:00:07.838494Z", - "shell.execute_reply": "2023-08-22T07:00:07.838064Z" - } + "execution": {} }, "outputs": [ { @@ -911,12 +811,7 @@ "execution_count": 21, "id": "214d0f23", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:07.840443Z", - "iopub.status.busy": "2023-08-22T07:00:07.840298Z", - "iopub.status.idle": "2023-08-22T07:00:07.842812Z", - "shell.execute_reply": "2023-08-22T07:00:07.842557Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -976,12 +871,7 @@ "execution_count": 22, "id": "ca7469fb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:07.844529Z", - "iopub.status.busy": "2023-08-22T07:00:07.844419Z", - "iopub.status.idle": "2023-08-22T07:00:07.900374Z", - "shell.execute_reply": "2023-08-22T07:00:07.899998Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1443,12 +1333,7 @@ "execution_count": 23, "id": "a5b4b3a7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:07.902654Z", - "iopub.status.busy": "2023-08-22T07:00:07.902513Z", - "iopub.status.idle": "2023-08-22T07:00:07.912659Z", - "shell.execute_reply": "2023-08-22T07:00:07.912286Z" - } + "execution": {} }, "outputs": [ { @@ -1865,12 +1750,7 @@ "execution_count": 24, "id": "ffdcd0c6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:07.914460Z", - "iopub.status.busy": "2023-08-22T07:00:07.914335Z", - "iopub.status.idle": "2023-08-22T07:00:08.105311Z", - "shell.execute_reply": "2023-08-22T07:00:08.104857Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1910,12 +1790,7 @@ "execution_count": 25, "id": "e60d63d3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.107307Z", - "iopub.status.busy": "2023-08-22T07:00:08.107179Z", - "iopub.status.idle": "2023-08-22T07:00:08.110477Z", - "shell.execute_reply": "2023-08-22T07:00:08.110049Z" - } + "execution": {} }, "outputs": [ { @@ -1967,12 +1842,7 @@ "execution_count": 26, "id": "a2253f89", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.112116Z", - "iopub.status.busy": "2023-08-22T07:00:08.112018Z", - "iopub.status.idle": "2023-08-22T07:00:08.114728Z", - "shell.execute_reply": "2023-08-22T07:00:08.114379Z" - } + "execution": {} }, "outputs": [ { @@ -2005,12 +1875,7 @@ "execution_count": 27, "id": "aa10fcf4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.116886Z", - "iopub.status.busy": "2023-08-22T07:00:08.116732Z", - "iopub.status.idle": "2023-08-22T07:00:08.119361Z", - "shell.execute_reply": "2023-08-22T07:00:08.119014Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2046,12 +1911,7 @@ "execution_count": 28, "id": "f5bc4121", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.121086Z", - "iopub.status.busy": "2023-08-22T07:00:08.120969Z", - "iopub.status.idle": "2023-08-22T07:00:08.133376Z", - "shell.execute_reply": "2023-08-22T07:00:08.133011Z" - } + "execution": {} }, "outputs": [ { @@ -2099,12 +1959,7 @@ "execution_count": 29, "id": "e4018437", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.135390Z", - "iopub.status.busy": "2023-08-22T07:00:08.135249Z", - "iopub.status.idle": "2023-08-22T07:00:08.138028Z", - "shell.execute_reply": "2023-08-22T07:00:08.137669Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2146,12 +2001,7 @@ "execution_count": 30, "id": "aaa73183", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.140104Z", - "iopub.status.busy": "2023-08-22T07:00:08.139927Z", - "iopub.status.idle": "2023-08-22T07:00:08.147255Z", - "shell.execute_reply": "2023-08-22T07:00:08.146900Z" - } + "execution": {} }, "outputs": [ { @@ -2204,12 +2054,7 @@ "execution_count": 31, "id": "8f95689e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.149814Z", - "iopub.status.busy": "2023-08-22T07:00:08.149544Z", - "iopub.status.idle": "2023-08-22T07:00:08.157631Z", - "shell.execute_reply": "2023-08-22T07:00:08.157328Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2261,12 +2106,7 @@ "execution_count": 32, "id": "725a3200", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.159712Z", - "iopub.status.busy": "2023-08-22T07:00:08.159508Z", - "iopub.status.idle": "2023-08-22T07:00:08.559312Z", - "shell.execute_reply": "2023-08-22T07:00:08.558977Z" - } + "execution": {} }, "outputs": [ { @@ -2518,12 +2358,7 @@ "execution_count": 33, "id": "823982cc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:08.561312Z", - "iopub.status.busy": "2023-08-22T07:00:08.561182Z", - "iopub.status.idle": "2023-08-22T07:00:11.194918Z", - "shell.execute_reply": "2023-08-22T07:00:11.194585Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3576,12 +3411,7 @@ "execution_count": 34, "id": "b1b7d3b4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:11.196745Z", - "iopub.status.busy": "2023-08-22T07:00:11.196604Z", - "iopub.status.idle": "2023-08-22T07:00:11.287223Z", - "shell.execute_reply": "2023-08-22T07:00:11.286902Z" - } + "execution": {} }, "outputs": [ { @@ -3620,12 +3450,7 @@ "execution_count": 35, "id": "59a69421", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:11.289077Z", - "iopub.status.busy": "2023-08-22T07:00:11.288949Z", - "iopub.status.idle": "2023-08-22T07:00:13.927873Z", - "shell.execute_reply": "2023-08-22T07:00:13.927443Z" - } + "execution": {} }, "outputs": [ { @@ -4710,12 +4535,7 @@ "execution_count": 36, "id": "572b14c3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:13.930022Z", - "iopub.status.busy": "2023-08-22T07:00:13.929865Z", - "iopub.status.idle": "2023-08-22T07:00:14.018201Z", - "shell.execute_reply": "2023-08-22T07:00:14.017805Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -4759,12 +4579,7 @@ "execution_count": 37, "id": "a6129b36", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.020355Z", - "iopub.status.busy": "2023-08-22T07:00:14.020186Z", - "iopub.status.idle": "2023-08-22T07:00:14.305398Z", - "shell.execute_reply": "2023-08-22T07:00:14.305076Z" - } + "execution": {} }, "outputs": [ { @@ -6862,12 +6677,7 @@ "execution_count": 38, "id": "3683157f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.307687Z", - "iopub.status.busy": "2023-08-22T07:00:14.307502Z", - "iopub.status.idle": "2023-08-22T07:00:14.399739Z", - "shell.execute_reply": "2023-08-22T07:00:14.399295Z" - } + "execution": {} }, "outputs": [ { @@ -6909,12 +6719,7 @@ "execution_count": 39, "id": "1d504d1b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.401869Z", - "iopub.status.busy": "2023-08-22T07:00:14.401710Z", - "iopub.status.idle": "2023-08-22T07:00:14.404500Z", - "shell.execute_reply": "2023-08-22T07:00:14.404205Z" - } + "execution": {} }, "outputs": [ { @@ -6948,12 +6753,7 @@ "execution_count": 40, "id": "8503f6ed", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.406204Z", - "iopub.status.busy": "2023-08-22T07:00:14.406092Z", - "iopub.status.idle": "2023-08-22T07:00:14.408483Z", - "shell.execute_reply": "2023-08-22T07:00:14.408172Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7013,12 +6813,7 @@ "execution_count": 41, "id": "47ab71ff", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.410278Z", - "iopub.status.busy": "2023-08-22T07:00:14.410146Z", - "iopub.status.idle": "2023-08-22T07:00:14.412596Z", - "shell.execute_reply": "2023-08-22T07:00:14.412250Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -7040,12 +6835,7 @@ "execution_count": 42, "id": "825b7073", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.414295Z", - "iopub.status.busy": "2023-08-22T07:00:14.414146Z", - "iopub.status.idle": "2023-08-22T07:00:14.676526Z", - "shell.execute_reply": "2023-08-22T07:00:14.676174Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -9106,12 +8896,7 @@ "execution_count": 43, "id": "efb2d25e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.678953Z", - "iopub.status.busy": "2023-08-22T07:00:14.678802Z", - "iopub.status.idle": "2023-08-22T07:00:14.728316Z", - "shell.execute_reply": "2023-08-22T07:00:14.727946Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -9142,12 +8927,7 @@ "execution_count": 44, "id": "5d2977a4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.730133Z", - "iopub.status.busy": "2023-08-22T07:00:14.730000Z", - "iopub.status.idle": "2023-08-22T07:00:14.740248Z", - "shell.execute_reply": "2023-08-22T07:00:14.739889Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -9176,12 +8956,7 @@ "execution_count": 45, "id": "0d833201", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.742261Z", - "iopub.status.busy": "2023-08-22T07:00:14.742118Z", - "iopub.status.idle": "2023-08-22T07:00:14.896514Z", - "shell.execute_reply": "2023-08-22T07:00:14.896137Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -9219,12 +8994,7 @@ "execution_count": 46, "id": "bfbf2fe3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.898477Z", - "iopub.status.busy": "2023-08-22T07:00:14.898358Z", - "iopub.status.idle": "2023-08-22T07:00:14.900774Z", - "shell.execute_reply": "2023-08-22T07:00:14.900366Z" - } + "execution": {} }, "outputs": [ { @@ -9255,12 +9025,7 @@ "execution_count": 47, "id": "ccb9a209", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.902539Z", - "iopub.status.busy": "2023-08-22T07:00:14.902374Z", - "iopub.status.idle": "2023-08-22T07:00:14.997062Z", - "shell.execute_reply": "2023-08-22T07:00:14.996746Z" - } + "execution": {} }, "outputs": [ { @@ -9302,12 +9067,7 @@ "execution_count": 48, "id": "40eff15b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:14.998878Z", - "iopub.status.busy": "2023-08-22T07:00:14.998760Z", - "iopub.status.idle": "2023-08-22T07:00:15.001284Z", - "shell.execute_reply": "2023-08-22T07:00:15.000943Z" - } + "execution": {} }, "outputs": [ { @@ -9372,12 +9132,7 @@ "execution_count": 49, "id": "2bd0cc9c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.003376Z", - "iopub.status.busy": "2023-08-22T07:00:15.003216Z", - "iopub.status.idle": "2023-08-22T07:00:15.007328Z", - "shell.execute_reply": "2023-08-22T07:00:15.007008Z" - } + "execution": {} }, "outputs": [ { @@ -9416,12 +9171,7 @@ "execution_count": 50, "id": "f188c54a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.008975Z", - "iopub.status.busy": "2023-08-22T07:00:15.008880Z", - "iopub.status.idle": "2023-08-22T07:00:15.012449Z", - "shell.execute_reply": "2023-08-22T07:00:15.012179Z" - } + "execution": {} }, "outputs": [ { @@ -9459,12 +9209,7 @@ "execution_count": 51, "id": "8be53659", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.014465Z", - "iopub.status.busy": "2023-08-22T07:00:15.014315Z", - "iopub.status.idle": "2023-08-22T07:00:15.127942Z", - "shell.execute_reply": "2023-08-22T07:00:15.127535Z" - } + "execution": {} }, "outputs": [ { @@ -9519,12 +9264,7 @@ "execution_count": 52, "id": "b6c30e1c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.129800Z", - "iopub.status.busy": "2023-08-22T07:00:15.129659Z", - "iopub.status.idle": "2023-08-22T07:00:15.218853Z", - "shell.execute_reply": "2023-08-22T07:00:15.218439Z" - } + "execution": {} }, "outputs": [ { @@ -9574,12 +9314,7 @@ "execution_count": 53, "id": "6fedf71f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.220829Z", - "iopub.status.busy": "2023-08-22T07:00:15.220699Z", - "iopub.status.idle": "2023-08-22T07:00:15.226460Z", - "shell.execute_reply": "2023-08-22T07:00:15.226111Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -9620,12 +9355,7 @@ "execution_count": 54, "id": "f78e9153", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.228509Z", - "iopub.status.busy": "2023-08-22T07:00:15.228362Z", - "iopub.status.idle": "2023-08-22T07:00:15.230933Z", - "shell.execute_reply": "2023-08-22T07:00:15.230633Z" - } + "execution": {} }, "outputs": [ { @@ -9675,12 +9405,7 @@ "execution_count": 55, "id": "31120c88", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.232659Z", - "iopub.status.busy": "2023-08-22T07:00:15.232549Z", - "iopub.status.idle": "2023-08-22T07:00:15.235970Z", - "shell.execute_reply": "2023-08-22T07:00:15.235650Z" - } + "execution": {} }, "outputs": [ { @@ -9717,12 +9442,7 @@ "execution_count": 56, "id": "fc6b3a12", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.237863Z", - "iopub.status.busy": "2023-08-22T07:00:15.237759Z", - "iopub.status.idle": "2023-08-22T07:00:15.333920Z", - "shell.execute_reply": "2023-08-22T07:00:15.333623Z" - } + "execution": {} }, "outputs": [ { @@ -9770,12 +9490,7 @@ "execution_count": 57, "id": "b806f0e8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:15.335731Z", - "iopub.status.busy": "2023-08-22T07:00:15.335610Z", - "iopub.status.idle": "2023-08-22T07:00:15.425776Z", - "shell.execute_reply": "2023-08-22T07:00:15.425408Z" - } + "execution": {} }, "outputs": [ { diff --git a/Ch07-nonlin-lab.ipynb b/Ch07-nonlin-lab.ipynb index 347036d..397b203 100644 --- a/Ch07-nonlin-lab.ipynb +++ b/Ch07-nonlin-lab.ipynb @@ -20,12 +20,7 @@ "execution_count": 1, "id": "f0d9a8c8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:19.101721Z", - "iopub.status.busy": "2023-08-22T07:00:19.101624Z", - "iopub.status.idle": "2023-08-22T07:00:20.128515Z", - "shell.execute_reply": "2023-08-22T07:00:20.128166Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -54,12 +49,7 @@ "execution_count": 2, "id": "c4bc71b4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.130689Z", - "iopub.status.busy": "2023-08-22T07:00:20.130382Z", - "iopub.status.idle": "2023-08-22T07:00:20.144261Z", - "shell.execute_reply": "2023-08-22T07:00:20.143915Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -93,12 +83,7 @@ "execution_count": 3, "id": "14649fd7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.146307Z", - "iopub.status.busy": "2023-08-22T07:00:20.146145Z", - "iopub.status.idle": "2023-08-22T07:00:20.154062Z", - "shell.execute_reply": "2023-08-22T07:00:20.153676Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -123,12 +108,7 @@ "execution_count": 4, "id": "bca84aa3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.155897Z", - "iopub.status.busy": "2023-08-22T07:00:20.155777Z", - "iopub.status.idle": "2023-08-22T07:00:20.258666Z", - "shell.execute_reply": "2023-08-22T07:00:20.253837Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -258,12 +238,7 @@ "execution_count": 5, "id": "411af5ab", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.267608Z", - "iopub.status.busy": "2023-08-22T07:00:20.265087Z", - "iopub.status.idle": "2023-08-22T07:00:20.272250Z", - "shell.execute_reply": "2023-08-22T07:00:20.271081Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -293,12 +268,7 @@ "execution_count": 6, "id": "0eb6317c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.278663Z", - "iopub.status.busy": "2023-08-22T07:00:20.278281Z", - "iopub.status.idle": "2023-08-22T07:00:20.286475Z", - "shell.execute_reply": "2023-08-22T07:00:20.285277Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -350,12 +320,7 @@ "execution_count": 7, "id": "714f2c6d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.291120Z", - "iopub.status.busy": "2023-08-22T07:00:20.289650Z", - "iopub.status.idle": "2023-08-22T07:00:20.490780Z", - "shell.execute_reply": "2023-08-22T07:00:20.489475Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -419,12 +384,7 @@ "execution_count": 8, "id": "0f5f60ed", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.501016Z", - "iopub.status.busy": "2023-08-22T07:00:20.500451Z", - "iopub.status.idle": "2023-08-22T07:00:20.542279Z", - "shell.execute_reply": "2023-08-22T07:00:20.541055Z" - } + "execution": {} }, "outputs": [ { @@ -559,12 +519,7 @@ "execution_count": 9, "id": "3ca7417d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.547883Z", - "iopub.status.busy": "2023-08-22T07:00:20.546804Z", - "iopub.status.idle": "2023-08-22T07:00:20.558851Z", - "shell.execute_reply": "2023-08-22T07:00:20.557712Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -668,12 +623,7 @@ "execution_count": 10, "id": "caadfcc3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.563550Z", - "iopub.status.busy": "2023-08-22T07:00:20.562723Z", - "iopub.status.idle": "2023-08-22T07:00:20.568989Z", - "shell.execute_reply": "2023-08-22T07:00:20.568096Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -709,12 +659,7 @@ "execution_count": 11, "id": "92de2600", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.574149Z", - "iopub.status.busy": "2023-08-22T07:00:20.573276Z", - "iopub.status.idle": "2023-08-22T07:00:20.605121Z", - "shell.execute_reply": "2023-08-22T07:00:20.604136Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -819,12 +764,7 @@ "execution_count": 12, "id": "a4452162", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.609844Z", - "iopub.status.busy": "2023-08-22T07:00:20.609519Z", - "iopub.status.idle": "2023-08-22T07:00:20.635472Z", - "shell.execute_reply": "2023-08-22T07:00:20.634538Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -932,12 +872,7 @@ "execution_count": 13, "id": "5b317cb2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.644474Z", - "iopub.status.busy": "2023-08-22T07:00:20.644125Z", - "iopub.status.idle": "2023-08-22T07:00:20.652869Z", - "shell.execute_reply": "2023-08-22T07:00:20.652118Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -959,12 +894,7 @@ "execution_count": 14, "id": "ba682884", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.659807Z", - "iopub.status.busy": "2023-08-22T07:00:20.659436Z", - "iopub.status.idle": "2023-08-22T07:00:20.794764Z", - "shell.execute_reply": "2023-08-22T07:00:20.793804Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1024,12 +954,7 @@ "execution_count": 15, "id": "84c211b3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.802014Z", - "iopub.status.busy": "2023-08-22T07:00:20.801524Z", - "iopub.status.idle": "2023-08-22T07:00:20.820547Z", - "shell.execute_reply": "2023-08-22T07:00:20.819213Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1153,12 +1078,7 @@ "execution_count": 16, "id": "60466a94", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.827052Z", - "iopub.status.busy": "2023-08-22T07:00:20.826622Z", - "iopub.status.idle": "2023-08-22T07:00:20.840226Z", - "shell.execute_reply": "2023-08-22T07:00:20.838401Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1196,12 +1116,7 @@ "execution_count": 17, "id": "ff6d9fcb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.847470Z", - "iopub.status.busy": "2023-08-22T07:00:20.846698Z", - "iopub.status.idle": "2023-08-22T07:00:20.876785Z", - "shell.execute_reply": "2023-08-22T07:00:20.874921Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1322,12 +1237,7 @@ "execution_count": 18, "id": "2a206718", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.884203Z", - "iopub.status.busy": "2023-08-22T07:00:20.883987Z", - "iopub.status.idle": "2023-08-22T07:00:20.912686Z", - "shell.execute_reply": "2023-08-22T07:00:20.910961Z" - } + "execution": {} }, "outputs": [ { @@ -1458,12 +1368,7 @@ "execution_count": 19, "id": "766241f2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.920607Z", - "iopub.status.busy": "2023-08-22T07:00:20.918963Z", - "iopub.status.idle": "2023-08-22T07:00:20.929300Z", - "shell.execute_reply": "2023-08-22T07:00:20.928186Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1503,12 +1408,7 @@ "execution_count": 20, "id": "71b3ac84", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.943822Z", - "iopub.status.busy": "2023-08-22T07:00:20.943014Z", - "iopub.status.idle": "2023-08-22T07:00:20.968025Z", - "shell.execute_reply": "2023-08-22T07:00:20.966003Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1637,12 +1537,7 @@ "execution_count": 21, "id": "eb35ef98", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:20.975032Z", - "iopub.status.busy": "2023-08-22T07:00:20.974773Z", - "iopub.status.idle": "2023-08-22T07:00:21.001621Z", - "shell.execute_reply": "2023-08-22T07:00:20.999410Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1754,12 +1649,7 @@ "execution_count": 22, "id": "78b393fe", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:21.009111Z", - "iopub.status.busy": "2023-08-22T07:00:21.008401Z", - "iopub.status.idle": "2023-08-22T07:00:21.127979Z", - "shell.execute_reply": "2023-08-22T07:00:21.126680Z" - } + "execution": {} }, "outputs": [ { @@ -1802,12 +1692,7 @@ "execution_count": 23, "id": "cb014cc6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:21.135155Z", - "iopub.status.busy": "2023-08-22T07:00:21.134355Z", - "iopub.status.idle": "2023-08-22T07:00:21.153025Z", - "shell.execute_reply": "2023-08-22T07:00:21.152246Z" - } + "execution": {} }, "outputs": [ { @@ -1848,12 +1733,7 @@ "execution_count": 24, "id": "106dc178", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:21.159707Z", - "iopub.status.busy": "2023-08-22T07:00:21.159506Z", - "iopub.status.idle": "2023-08-22T07:00:21.446559Z", - "shell.execute_reply": "2023-08-22T07:00:21.445625Z" - } + "execution": {} }, "outputs": [ { @@ -1894,12 +1774,7 @@ "execution_count": 25, "id": "d0334df0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:21.454580Z", - "iopub.status.busy": "2023-08-22T07:00:21.453805Z", - "iopub.status.idle": "2023-08-22T07:00:21.741596Z", - "shell.execute_reply": "2023-08-22T07:00:21.740381Z" - } + "execution": {} }, "outputs": [ { @@ -1949,12 +1824,7 @@ "execution_count": 26, "id": "e00ec554", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:21.748135Z", - "iopub.status.busy": "2023-08-22T07:00:21.747093Z", - "iopub.status.idle": "2023-08-22T07:00:21.771304Z", - "shell.execute_reply": "2023-08-22T07:00:21.768895Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1991,12 +1861,7 @@ "execution_count": 27, "id": "28e301a2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:21.776636Z", - "iopub.status.busy": "2023-08-22T07:00:21.775975Z", - "iopub.status.idle": "2023-08-22T07:00:22.088656Z", - "shell.execute_reply": "2023-08-22T07:00:22.087101Z" - } + "execution": {} }, "outputs": [ { @@ -2053,12 +1918,7 @@ "execution_count": 28, "id": "7988af75", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.093999Z", - "iopub.status.busy": "2023-08-22T07:00:22.092990Z", - "iopub.status.idle": "2023-08-22T07:00:22.104732Z", - "shell.execute_reply": "2023-08-22T07:00:22.103894Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -2092,12 +1952,7 @@ "execution_count": 29, "id": "1a4803f3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.111630Z", - "iopub.status.busy": "2023-08-22T07:00:22.109538Z", - "iopub.status.idle": "2023-08-22T07:00:22.238185Z", - "shell.execute_reply": "2023-08-22T07:00:22.237011Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2155,12 +2010,7 @@ "execution_count": 30, "id": "dc655431", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.242955Z", - "iopub.status.busy": "2023-08-22T07:00:22.242470Z", - "iopub.status.idle": "2023-08-22T07:00:22.377106Z", - "shell.execute_reply": "2023-08-22T07:00:22.375348Z" - } + "execution": {} }, "outputs": [ { @@ -2216,12 +2066,7 @@ "execution_count": 31, "id": "90ea4ff0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.382764Z", - "iopub.status.busy": "2023-08-22T07:00:22.381824Z", - "iopub.status.idle": "2023-08-22T07:00:22.403585Z", - "shell.execute_reply": "2023-08-22T07:00:22.401813Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -2253,12 +2098,7 @@ "execution_count": 32, "id": "104bc542", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.408939Z", - "iopub.status.busy": "2023-08-22T07:00:22.408540Z", - "iopub.status.idle": "2023-08-22T07:00:22.524209Z", - "shell.execute_reply": "2023-08-22T07:00:22.522972Z" - } + "execution": {} }, "outputs": [ { @@ -2296,12 +2136,7 @@ "execution_count": 33, "id": "d5884f39", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.529074Z", - "iopub.status.busy": "2023-08-22T07:00:22.528274Z", - "iopub.status.idle": "2023-08-22T07:00:22.562860Z", - "shell.execute_reply": "2023-08-22T07:00:22.561973Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -2329,12 +2164,7 @@ "execution_count": 34, "id": "9155767c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.569210Z", - "iopub.status.busy": "2023-08-22T07:00:22.568849Z", - "iopub.status.idle": "2023-08-22T07:00:22.677881Z", - "shell.execute_reply": "2023-08-22T07:00:22.676932Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2382,12 +2212,7 @@ "execution_count": 35, "id": "048524d1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.685366Z", - "iopub.status.busy": "2023-08-22T07:00:22.684753Z", - "iopub.status.idle": "2023-08-22T07:00:22.790525Z", - "shell.execute_reply": "2023-08-22T07:00:22.789666Z" - } + "execution": {} }, "outputs": [ { @@ -2429,12 +2254,7 @@ "execution_count": 36, "id": "3d632d24", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.797262Z", - "iopub.status.busy": "2023-08-22T07:00:22.796333Z", - "iopub.status.idle": "2023-08-22T07:00:22.839603Z", - "shell.execute_reply": "2023-08-22T07:00:22.837625Z" - } + "execution": {} }, "outputs": [ { @@ -2476,12 +2296,7 @@ "execution_count": 37, "id": "f5e21a13", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.850660Z", - "iopub.status.busy": "2023-08-22T07:00:22.849974Z", - "iopub.status.idle": "2023-08-22T07:00:22.858653Z", - "shell.execute_reply": "2023-08-22T07:00:22.857543Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2583,12 +2398,7 @@ "execution_count": 38, "id": "6bc84bd9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.866946Z", - "iopub.status.busy": "2023-08-22T07:00:22.866205Z", - "iopub.status.idle": "2023-08-22T07:00:22.902537Z", - "shell.execute_reply": "2023-08-22T07:00:22.901746Z" - } + "execution": {} }, "outputs": [ { @@ -2693,12 +2503,7 @@ "execution_count": 39, "id": "dd9e0491", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.910365Z", - "iopub.status.busy": "2023-08-22T07:00:22.909672Z", - "iopub.status.idle": "2023-08-22T07:00:22.916982Z", - "shell.execute_reply": "2023-08-22T07:00:22.915954Z" - } + "execution": {} }, "outputs": [ { @@ -2766,12 +2571,7 @@ "execution_count": 40, "id": "cb3cb4ef", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.924618Z", - "iopub.status.busy": "2023-08-22T07:00:22.924291Z", - "iopub.status.idle": "2023-08-22T07:00:22.938562Z", - "shell.execute_reply": "2023-08-22T07:00:22.937364Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2792,12 +2592,7 @@ "execution_count": 41, "id": "583711cb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:22.946138Z", - "iopub.status.busy": "2023-08-22T07:00:22.945678Z", - "iopub.status.idle": "2023-08-22T07:00:23.056404Z", - "shell.execute_reply": "2023-08-22T07:00:23.054342Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2826,12 +2621,7 @@ "execution_count": 42, "id": "259d273b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.062645Z", - "iopub.status.busy": "2023-08-22T07:00:23.062312Z", - "iopub.status.idle": "2023-08-22T07:00:23.191981Z", - "shell.execute_reply": "2023-08-22T07:00:23.190477Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2870,12 +2660,7 @@ "execution_count": 43, "id": "71dde739", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.199542Z", - "iopub.status.busy": "2023-08-22T07:00:23.199201Z", - "iopub.status.idle": "2023-08-22T07:00:23.217281Z", - "shell.execute_reply": "2023-08-22T07:00:23.216211Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2979,12 +2764,7 @@ "execution_count": 44, "id": "126b4433", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.225140Z", - "iopub.status.busy": "2023-08-22T07:00:23.224675Z", - "iopub.status.idle": "2023-08-22T07:00:23.231622Z", - "shell.execute_reply": "2023-08-22T07:00:23.230728Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3013,12 +2793,7 @@ "execution_count": 45, "id": "71357343", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.238285Z", - "iopub.status.busy": "2023-08-22T07:00:23.237444Z", - "iopub.status.idle": "2023-08-22T07:00:23.300732Z", - "shell.execute_reply": "2023-08-22T07:00:23.299079Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3056,12 +2831,7 @@ "execution_count": 46, "id": "568fe30f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.308132Z", - "iopub.status.busy": "2023-08-22T07:00:23.307471Z", - "iopub.status.idle": "2023-08-22T07:00:23.412833Z", - "shell.execute_reply": "2023-08-22T07:00:23.411958Z" - } + "execution": {} }, "outputs": [ { @@ -3090,12 +2860,7 @@ "execution_count": 47, "id": "56f3acef", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.419875Z", - "iopub.status.busy": "2023-08-22T07:00:23.419189Z", - "iopub.status.idle": "2023-08-22T07:00:23.528901Z", - "shell.execute_reply": "2023-08-22T07:00:23.527531Z" - } + "execution": {} }, "outputs": [ { @@ -3123,12 +2888,7 @@ "execution_count": 48, "id": "74d23615", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.533975Z", - "iopub.status.busy": "2023-08-22T07:00:23.533655Z", - "iopub.status.idle": "2023-08-22T07:00:23.643883Z", - "shell.execute_reply": "2023-08-22T07:00:23.643400Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3171,12 +2931,7 @@ "execution_count": 49, "id": "5a0046bf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:23.646013Z", - "iopub.status.busy": "2023-08-22T07:00:23.645883Z", - "iopub.status.idle": "2023-08-22T07:00:23.774046Z", - "shell.execute_reply": "2023-08-22T07:00:23.773509Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ diff --git a/Ch08-baggboost-lab.ipynb b/Ch08-baggboost-lab.ipynb index b378a78..bb098cf 100644 --- a/Ch08-baggboost-lab.ipynb +++ b/Ch08-baggboost-lab.ipynb @@ -25,12 +25,7 @@ "execution_count": 1, "id": "5061d7d5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:27.086529Z", - "iopub.status.busy": "2023-08-22T07:00:27.086444Z", - "iopub.status.idle": "2023-08-22T07:00:28.125805Z", - "shell.execute_reply": "2023-08-22T07:00:28.125477Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -57,12 +52,7 @@ "execution_count": 2, "id": "747b056a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.127941Z", - "iopub.status.busy": "2023-08-22T07:00:28.127762Z", - "iopub.status.idle": "2023-08-22T07:00:28.677332Z", - "shell.execute_reply": "2023-08-22T07:00:28.676967Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -105,12 +95,7 @@ "execution_count": 3, "id": "a29167fd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.679410Z", - "iopub.status.busy": "2023-08-22T07:00:28.679295Z", - "iopub.status.idle": "2023-08-22T07:00:28.684543Z", - "shell.execute_reply": "2023-08-22T07:00:28.684233Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -136,12 +121,7 @@ "execution_count": 4, "id": "9fc6c8b9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.686277Z", - "iopub.status.busy": "2023-08-22T07:00:28.686146Z", - "iopub.status.idle": "2023-08-22T07:00:28.699975Z", - "shell.execute_reply": "2023-08-22T07:00:28.699627Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -171,12 +151,7 @@ "execution_count": 5, "id": "533f0949", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.702236Z", - "iopub.status.busy": "2023-08-22T07:00:28.702106Z", - "iopub.status.idle": "2023-08-22T07:00:28.707600Z", - "shell.execute_reply": "2023-08-22T07:00:28.707288Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -223,12 +198,7 @@ "execution_count": 6, "id": "c4a8718e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.709124Z", - "iopub.status.busy": "2023-08-22T07:00:28.709024Z", - "iopub.status.idle": "2023-08-22T07:00:28.711727Z", - "shell.execute_reply": "2023-08-22T07:00:28.711484Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -270,12 +240,7 @@ "execution_count": 7, "id": "2fe92cb1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.713483Z", - "iopub.status.busy": "2023-08-22T07:00:28.713358Z", - "iopub.status.idle": "2023-08-22T07:00:28.716712Z", - "shell.execute_reply": "2023-08-22T07:00:28.716377Z" - } + "execution": {} }, "outputs": [ { @@ -313,12 +278,7 @@ "execution_count": 8, "id": "823745dc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.718214Z", - "iopub.status.busy": "2023-08-22T07:00:28.718128Z", - "iopub.status.idle": "2023-08-22T07:00:28.972065Z", - "shell.execute_reply": "2023-08-22T07:00:28.971661Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -361,12 +321,7 @@ "execution_count": 9, "id": "38ec5f13", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.974012Z", - "iopub.status.busy": "2023-08-22T07:00:28.973872Z", - "iopub.status.idle": "2023-08-22T07:00:28.976664Z", - "shell.execute_reply": "2023-08-22T07:00:28.976277Z" - } + "execution": {} }, "outputs": [ { @@ -426,12 +381,7 @@ "execution_count": 10, "id": "3959f39a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.978468Z", - "iopub.status.busy": "2023-08-22T07:00:28.978343Z", - "iopub.status.idle": "2023-08-22T07:00:28.984086Z", - "shell.execute_reply": "2023-08-22T07:00:28.983816Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -482,12 +432,7 @@ "execution_count": 11, "id": "201c4690", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.986102Z", - "iopub.status.busy": "2023-08-22T07:00:28.985936Z", - "iopub.status.idle": "2023-08-22T07:00:28.988314Z", - "shell.execute_reply": "2023-08-22T07:00:28.988003Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -515,12 +460,7 @@ "execution_count": 12, "id": "a8dc5c3a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.990208Z", - "iopub.status.busy": "2023-08-22T07:00:28.990083Z", - "iopub.status.idle": "2023-08-22T07:00:28.994160Z", - "shell.execute_reply": "2023-08-22T07:00:28.993813Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -555,12 +495,7 @@ "execution_count": 13, "id": "0cbe0d28", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:28.996199Z", - "iopub.status.busy": "2023-08-22T07:00:28.996086Z", - "iopub.status.idle": "2023-08-22T07:00:28.999517Z", - "shell.execute_reply": "2023-08-22T07:00:28.999166Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -585,12 +520,7 @@ "execution_count": 14, "id": "ea649080", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:29.001372Z", - "iopub.status.busy": "2023-08-22T07:00:29.001246Z", - "iopub.status.idle": "2023-08-22T07:00:29.236635Z", - "shell.execute_reply": "2023-08-22T07:00:29.236312Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -628,12 +558,7 @@ "execution_count": 15, "id": "e005da14", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:29.238455Z", - "iopub.status.busy": "2023-08-22T07:00:29.238325Z", - "iopub.status.idle": "2023-08-22T07:00:29.937364Z", - "shell.execute_reply": "2023-08-22T07:00:29.937016Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -670,12 +595,7 @@ "execution_count": 16, "id": "23324f7e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:29.939343Z", - "iopub.status.busy": "2023-08-22T07:00:29.939187Z", - "iopub.status.idle": "2023-08-22T07:00:29.942069Z", - "shell.execute_reply": "2023-08-22T07:00:29.941716Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -710,12 +630,7 @@ "execution_count": 17, "id": "6dcd8b37", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:29.943944Z", - "iopub.status.busy": "2023-08-22T07:00:29.943797Z", - "iopub.status.idle": "2023-08-22T07:00:29.951177Z", - "shell.execute_reply": "2023-08-22T07:00:29.950818Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -816,12 +731,7 @@ "execution_count": 18, "id": "0459d3a9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:29.953197Z", - "iopub.status.busy": "2023-08-22T07:00:29.952962Z", - "iopub.status.idle": "2023-08-22T07:00:29.965364Z", - "shell.execute_reply": "2023-08-22T07:00:29.965021Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -846,12 +756,7 @@ "execution_count": 19, "id": "34bf2864", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:29.967326Z", - "iopub.status.busy": "2023-08-22T07:00:29.967175Z", - "iopub.status.idle": "2023-08-22T07:00:29.969789Z", - "shell.execute_reply": "2023-08-22T07:00:29.969459Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -877,12 +782,7 @@ "execution_count": 20, "id": "dd0dfd8a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:29.971954Z", - "iopub.status.busy": "2023-08-22T07:00:29.971825Z", - "iopub.status.idle": "2023-08-22T07:00:30.219764Z", - "shell.execute_reply": "2023-08-22T07:00:30.219356Z" - } + "execution": {} }, "outputs": [ { @@ -931,12 +831,7 @@ "execution_count": 21, "id": "33fb7786", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:30.221897Z", - "iopub.status.busy": "2023-08-22T07:00:30.221737Z", - "iopub.status.idle": "2023-08-22T07:00:30.264092Z", - "shell.execute_reply": "2023-08-22T07:00:30.263767Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -966,12 +861,7 @@ "execution_count": 22, "id": "a386755b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:30.265998Z", - "iopub.status.busy": "2023-08-22T07:00:30.265875Z", - "iopub.status.idle": "2023-08-22T07:00:30.268622Z", - "shell.execute_reply": "2023-08-22T07:00:30.268322Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1012,12 +902,7 @@ "execution_count": 23, "id": "3d646928", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:30.270273Z", - "iopub.status.busy": "2023-08-22T07:00:30.270155Z", - "iopub.status.idle": "2023-08-22T07:00:30.522160Z", - "shell.execute_reply": "2023-08-22T07:00:30.521845Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1073,12 +958,7 @@ "execution_count": 24, "id": "275feef7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:30.524138Z", - "iopub.status.busy": "2023-08-22T07:00:30.524014Z", - "iopub.status.idle": "2023-08-22T07:00:30.657807Z", - "shell.execute_reply": "2023-08-22T07:00:30.657403Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1117,12 +997,7 @@ "execution_count": 25, "id": "01dbbef3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:30.660013Z", - "iopub.status.busy": "2023-08-22T07:00:30.659853Z", - "iopub.status.idle": "2023-08-22T07:00:30.742817Z", - "shell.execute_reply": "2023-08-22T07:00:30.742410Z" - } + "execution": {} }, "outputs": [ { @@ -1170,12 +1045,7 @@ "execution_count": 26, "id": "b75cc90e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:30.744837Z", - "iopub.status.busy": "2023-08-22T07:00:30.744692Z", - "iopub.status.idle": "2023-08-22T07:00:31.411169Z", - "shell.execute_reply": "2023-08-22T07:00:31.410861Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1219,12 +1089,7 @@ "execution_count": 27, "id": "bf9a5ed4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:31.412847Z", - "iopub.status.busy": "2023-08-22T07:00:31.412730Z", - "iopub.status.idle": "2023-08-22T07:00:31.503962Z", - "shell.execute_reply": "2023-08-22T07:00:31.503621Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1262,12 +1127,7 @@ "execution_count": 28, "id": "71316e9a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:31.505738Z", - "iopub.status.busy": "2023-08-22T07:00:31.505608Z", - "iopub.status.idle": "2023-08-22T07:00:31.511656Z", - "shell.execute_reply": "2023-08-22T07:00:31.511297Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1420,12 +1280,7 @@ "execution_count": 29, "id": "0bcc5ff1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:31.513595Z", - "iopub.status.busy": "2023-08-22T07:00:31.513453Z", - "iopub.status.idle": "2023-08-22T07:00:34.630915Z", - "shell.execute_reply": "2023-08-22T07:00:34.630577Z" - } + "execution": {} }, "outputs": [ { @@ -1468,12 +1323,7 @@ "execution_count": 30, "id": "060f47eb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:34.632952Z", - "iopub.status.busy": "2023-08-22T07:00:34.632784Z", - "iopub.status.idle": "2023-08-22T07:00:35.097134Z", - "shell.execute_reply": "2023-08-22T07:00:35.096800Z" - } + "execution": {} }, "outputs": [ { @@ -1518,12 +1368,7 @@ "execution_count": 31, "id": "43505dad", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:35.100329Z", - "iopub.status.busy": "2023-08-22T07:00:35.100143Z", - "iopub.status.idle": "2023-08-22T07:00:35.112033Z", - "shell.execute_reply": "2023-08-22T07:00:35.111605Z" - } + "execution": {} }, "outputs": [ { @@ -1559,12 +1404,7 @@ "execution_count": 32, "id": "c0a03126", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:35.113963Z", - "iopub.status.busy": "2023-08-22T07:00:35.113830Z", - "iopub.status.idle": "2023-08-22T07:00:37.129817Z", - "shell.execute_reply": "2023-08-22T07:00:37.129514Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1626,12 +1466,7 @@ "execution_count": 33, "id": "58f6e11f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:37.131701Z", - "iopub.status.busy": "2023-08-22T07:00:37.131582Z", - "iopub.status.idle": "2023-08-22T07:00:38.482392Z", - "shell.execute_reply": "2023-08-22T07:00:38.481994Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1667,12 +1502,7 @@ "execution_count": 34, "id": "a5b1296f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:38.485515Z", - "iopub.status.busy": "2023-08-22T07:00:38.485339Z", - "iopub.status.idle": "2023-08-22T07:00:38.830191Z", - "shell.execute_reply": "2023-08-22T07:00:38.829761Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1706,12 +1536,7 @@ "execution_count": 35, "id": "36c52755", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:38.832666Z", - "iopub.status.busy": "2023-08-22T07:00:38.832460Z", - "iopub.status.idle": "2023-08-22T07:00:38.836173Z", - "shell.execute_reply": "2023-08-22T07:00:38.835780Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ diff --git a/Ch09-svm-lab.ipynb b/Ch09-svm-lab.ipynb index 3b96de8..c496134 100644 --- a/Ch09-svm-lab.ipynb +++ b/Ch09-svm-lab.ipynb @@ -27,12 +27,7 @@ "execution_count": 1, "id": "3973b95f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:43.196408Z", - "iopub.status.busy": "2023-08-22T07:00:43.196299Z", - "iopub.status.idle": "2023-08-22T07:00:44.031971Z", - "shell.execute_reply": "2023-08-22T07:00:44.031635Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -57,12 +52,7 @@ "execution_count": 2, "id": "0161e55e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.034329Z", - "iopub.status.busy": "2023-08-22T07:00:44.034140Z", - "iopub.status.idle": "2023-08-22T07:00:44.066591Z", - "shell.execute_reply": "2023-08-22T07:00:44.066283Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -85,12 +75,7 @@ "execution_count": 3, "id": "7661b056", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.068359Z", - "iopub.status.busy": "2023-08-22T07:00:44.068256Z", - "iopub.status.idle": "2023-08-22T07:00:44.070030Z", - "shell.execute_reply": "2023-08-22T07:00:44.069763Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -125,12 +110,7 @@ "execution_count": 4, "id": "46e9ab84", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.071771Z", - "iopub.status.busy": "2023-08-22T07:00:44.071636Z", - "iopub.status.idle": "2023-08-22T07:00:44.163199Z", - "shell.execute_reply": "2023-08-22T07:00:44.162710Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -170,12 +150,7 @@ "execution_count": 5, "id": "605ffdc0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.165309Z", - "iopub.status.busy": "2023-08-22T07:00:44.165170Z", - "iopub.status.idle": "2023-08-22T07:00:44.169891Z", - "shell.execute_reply": "2023-08-22T07:00:44.169496Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -214,12 +189,7 @@ "execution_count": 6, "id": "302a49a1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.171704Z", - "iopub.status.busy": "2023-08-22T07:00:44.171421Z", - "iopub.status.idle": "2023-08-22T07:00:44.323926Z", - "shell.execute_reply": "2023-08-22T07:00:44.323505Z" - } + "execution": {} }, "outputs": [ { @@ -259,12 +229,7 @@ "execution_count": 7, "id": "cc1d6a13", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.326070Z", - "iopub.status.busy": "2023-08-22T07:00:44.325931Z", - "iopub.status.idle": "2023-08-22T07:00:44.449908Z", - "shell.execute_reply": "2023-08-22T07:00:44.449566Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -305,12 +270,7 @@ "execution_count": 8, "id": "6133c846", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.451882Z", - "iopub.status.busy": "2023-08-22T07:00:44.451759Z", - "iopub.status.idle": "2023-08-22T07:00:44.454360Z", - "shell.execute_reply": "2023-08-22T07:00:44.454092Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -343,12 +303,7 @@ "execution_count": 9, "id": "9adb3793", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.456205Z", - "iopub.status.busy": "2023-08-22T07:00:44.456086Z", - "iopub.status.idle": "2023-08-22T07:00:44.482752Z", - "shell.execute_reply": "2023-08-22T07:00:44.482392Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -391,12 +346,7 @@ "execution_count": 10, "id": "d3ab343e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.484837Z", - "iopub.status.busy": "2023-08-22T07:00:44.484685Z", - "iopub.status.idle": "2023-08-22T07:00:44.487229Z", - "shell.execute_reply": "2023-08-22T07:00:44.486932Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -432,12 +382,7 @@ "execution_count": 11, "id": "6aba117e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.488910Z", - "iopub.status.busy": "2023-08-22T07:00:44.488789Z", - "iopub.status.idle": "2023-08-22T07:00:44.490857Z", - "shell.execute_reply": "2023-08-22T07:00:44.490592Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -461,12 +406,7 @@ "execution_count": 12, "id": "dbe7d737", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.492721Z", - "iopub.status.busy": "2023-08-22T07:00:44.492565Z", - "iopub.status.idle": "2023-08-22T07:00:44.498024Z", - "shell.execute_reply": "2023-08-22T07:00:44.497699Z" - } + "execution": {} }, "outputs": [ { @@ -548,12 +488,7 @@ "execution_count": 13, "id": "ab1697c2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.499990Z", - "iopub.status.busy": "2023-08-22T07:00:44.499862Z", - "iopub.status.idle": "2023-08-22T07:00:44.504347Z", - "shell.execute_reply": "2023-08-22T07:00:44.504085Z" - } + "execution": {} }, "outputs": [ { @@ -639,12 +574,7 @@ "execution_count": 14, "id": "0fd42b1e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.506318Z", - "iopub.status.busy": "2023-08-22T07:00:44.506164Z", - "iopub.status.idle": "2023-08-22T07:00:44.587482Z", - "shell.execute_reply": "2023-08-22T07:00:44.587059Z" - } + "execution": {} }, "outputs": [ { @@ -677,12 +607,7 @@ "execution_count": 15, "id": "09c15299", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.589398Z", - "iopub.status.busy": "2023-08-22T07:00:44.589260Z", - "iopub.status.idle": "2023-08-22T07:00:44.594250Z", - "shell.execute_reply": "2023-08-22T07:00:44.593884Z" - } + "execution": {} }, "outputs": [ { @@ -764,12 +689,7 @@ "execution_count": 16, "id": "d5fd2ff9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.596069Z", - "iopub.status.busy": "2023-08-22T07:00:44.595957Z", - "iopub.status.idle": "2023-08-22T07:00:44.704810Z", - "shell.execute_reply": "2023-08-22T07:00:44.704515Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -809,12 +729,7 @@ "execution_count": 17, "id": "39aff1b1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.706840Z", - "iopub.status.busy": "2023-08-22T07:00:44.706674Z", - "iopub.status.idle": "2023-08-22T07:00:44.711712Z", - "shell.execute_reply": "2023-08-22T07:00:44.711351Z" - } + "execution": {} }, "outputs": [ { @@ -896,12 +811,7 @@ "execution_count": 18, "id": "63a9d752", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.713664Z", - "iopub.status.busy": "2023-08-22T07:00:44.713515Z", - "iopub.status.idle": "2023-08-22T07:00:44.825679Z", - "shell.execute_reply": "2023-08-22T07:00:44.825364Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -949,12 +859,7 @@ "execution_count": 19, "id": "2fee8df5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.827655Z", - "iopub.status.busy": "2023-08-22T07:00:44.827492Z", - "iopub.status.idle": "2023-08-22T07:00:44.829859Z", - "shell.execute_reply": "2023-08-22T07:00:44.829433Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -977,12 +882,7 @@ "execution_count": 20, "id": "48f01abe", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.831676Z", - "iopub.status.busy": "2023-08-22T07:00:44.831542Z", - "iopub.status.idle": "2023-08-22T07:00:44.912763Z", - "shell.execute_reply": "2023-08-22T07:00:44.912396Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1030,12 +930,7 @@ "execution_count": 21, "id": "4acc3246", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.914642Z", - "iopub.status.busy": "2023-08-22T07:00:44.914493Z", - "iopub.status.idle": "2023-08-22T07:00:44.918862Z", - "shell.execute_reply": "2023-08-22T07:00:44.918577Z" - } + "execution": {} }, "outputs": [ { @@ -1078,12 +973,7 @@ "execution_count": 22, "id": "e9852a28", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:44.920379Z", - "iopub.status.busy": "2023-08-22T07:00:44.920289Z", - "iopub.status.idle": "2023-08-22T07:00:45.229404Z", - "shell.execute_reply": "2023-08-22T07:00:45.229004Z" - } + "execution": {} }, "outputs": [ { @@ -1122,12 +1012,7 @@ "execution_count": 23, "id": "01232fc9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:45.231425Z", - "iopub.status.busy": "2023-08-22T07:00:45.231287Z", - "iopub.status.idle": "2023-08-22T07:00:45.394458Z", - "shell.execute_reply": "2023-08-22T07:00:45.394098Z" - } + "execution": {} }, "outputs": [ { @@ -1166,12 +1051,7 @@ "execution_count": 24, "id": "bcbd15a4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:45.396381Z", - "iopub.status.busy": "2023-08-22T07:00:45.396249Z", - "iopub.status.idle": "2023-08-22T07:00:45.487743Z", - "shell.execute_reply": "2023-08-22T07:00:45.487417Z" - } + "execution": {} }, "outputs": [ { @@ -1214,12 +1094,7 @@ "execution_count": 25, "id": "28ca551e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:45.489428Z", - "iopub.status.busy": "2023-08-22T07:00:45.489307Z", - "iopub.status.idle": "2023-08-22T07:00:45.761918Z", - "shell.execute_reply": "2023-08-22T07:00:45.761509Z" - } + "execution": {} }, "outputs": [ { @@ -1348,12 +1223,7 @@ "execution_count": 26, "id": "68ac9421", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:45.763931Z", - "iopub.status.busy": "2023-08-22T07:00:45.763759Z", - "iopub.status.idle": "2023-08-22T07:00:45.849017Z", - "shell.execute_reply": "2023-08-22T07:00:45.848298Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1393,12 +1263,7 @@ "execution_count": 27, "id": "f79a9e0a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:45.851590Z", - "iopub.status.busy": "2023-08-22T07:00:45.851425Z", - "iopub.status.idle": "2023-08-22T07:00:45.979876Z", - "shell.execute_reply": "2023-08-22T07:00:45.979437Z" - } + "execution": {} }, "outputs": [ { @@ -1442,12 +1307,7 @@ "execution_count": 28, "id": "bdb9e503", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:45.982037Z", - "iopub.status.busy": "2023-08-22T07:00:45.981884Z", - "iopub.status.idle": "2023-08-22T07:00:45.986135Z", - "shell.execute_reply": "2023-08-22T07:00:45.985717Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1473,12 +1333,7 @@ "execution_count": 29, "id": "329f5d2c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:45.988269Z", - "iopub.status.busy": "2023-08-22T07:00:45.988146Z", - "iopub.status.idle": "2023-08-22T07:00:46.085414Z", - "shell.execute_reply": "2023-08-22T07:00:46.084883Z" - } + "execution": {} }, "outputs": [ { @@ -1528,12 +1383,7 @@ "execution_count": 30, "id": "267e113d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:46.087864Z", - "iopub.status.busy": "2023-08-22T07:00:46.087700Z", - "iopub.status.idle": "2023-08-22T07:00:46.169591Z", - "shell.execute_reply": "2023-08-22T07:00:46.169145Z" - } + "execution": {} }, "outputs": [ { @@ -1569,12 +1419,7 @@ "execution_count": 31, "id": "64cbebd0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:46.171760Z", - "iopub.status.busy": "2023-08-22T07:00:46.171615Z", - "iopub.status.idle": "2023-08-22T07:00:46.921038Z", - "shell.execute_reply": "2023-08-22T07:00:46.920658Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1633,12 +1478,7 @@ "execution_count": 32, "id": "b6e6f12b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:46.923126Z", - "iopub.status.busy": "2023-08-22T07:00:46.922983Z", - "iopub.status.idle": "2023-08-22T07:00:46.997998Z", - "shell.execute_reply": "2023-08-22T07:00:46.997721Z" - } + "execution": {} }, "outputs": [ { @@ -1679,12 +1519,7 @@ "execution_count": 33, "id": "273a10b2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:47.000124Z", - "iopub.status.busy": "2023-08-22T07:00:46.999957Z", - "iopub.status.idle": "2023-08-22T07:00:47.027848Z", - "shell.execute_reply": "2023-08-22T07:00:47.027461Z" - } + "execution": {} }, "outputs": [ { @@ -1793,12 +1628,7 @@ "execution_count": 34, "id": "bc3079a7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:47.030032Z", - "iopub.status.busy": "2023-08-22T07:00:47.029880Z", - "iopub.status.idle": "2023-08-22T07:00:47.040946Z", - "shell.execute_reply": "2023-08-22T07:00:47.040538Z" - } + "execution": {} }, "outputs": [ { diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb index 4c5a709..32d85fc 100644 --- a/Ch10-deeplearning-lab.ipynb +++ b/Ch10-deeplearning-lab.ipynb @@ -26,12 +26,7 @@ "execution_count": 1, "id": "cf431f3f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:23.822772Z", - "iopub.status.busy": "2023-08-22T06:36:23.822498Z", - "iopub.status.idle": "2023-08-22T06:36:35.889421Z", - "shell.execute_reply": "2023-08-22T06:36:35.888914Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -69,12 +64,7 @@ "execution_count": 2, "id": "1db00e03", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:35.892370Z", - "iopub.status.busy": "2023-08-22T06:36:35.892133Z", - "iopub.status.idle": "2023-08-22T06:36:37.554519Z", - "shell.execute_reply": "2023-08-22T06:36:37.554232Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -102,12 +92,7 @@ "execution_count": 3, "id": "3da0a445", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:37.556320Z", - "iopub.status.busy": "2023-08-22T06:36:37.556190Z", - "iopub.status.idle": "2023-08-22T06:36:38.312094Z", - "shell.execute_reply": "2023-08-22T06:36:38.311799Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -133,12 +118,7 @@ "execution_count": 4, "id": "bbbf32fe", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.313997Z", - "iopub.status.busy": "2023-08-22T06:36:38.313882Z", - "iopub.status.idle": "2023-08-22T06:36:38.557850Z", - "shell.execute_reply": "2023-08-22T06:36:38.557519Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -160,12 +140,7 @@ "execution_count": 5, "id": "3810caf4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.559742Z", - "iopub.status.busy": "2023-08-22T06:36:38.559626Z", - "iopub.status.idle": "2023-08-22T06:36:38.563148Z", - "shell.execute_reply": "2023-08-22T06:36:38.562883Z" - } + "execution": {} }, "outputs": [ { @@ -197,12 +172,7 @@ "execution_count": 6, "id": "454dc419", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.564731Z", - "iopub.status.busy": "2023-08-22T06:36:38.564619Z", - "iopub.status.idle": "2023-08-22T06:36:38.566404Z", - "shell.execute_reply": "2023-08-22T06:36:38.566156Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -239,12 +209,7 @@ "execution_count": 7, "id": "cd43a4c6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.567754Z", - "iopub.status.busy": "2023-08-22T06:36:38.567677Z", - "iopub.status.idle": "2023-08-22T06:36:38.569838Z", - "shell.execute_reply": "2023-08-22T06:36:38.569588Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -275,12 +240,7 @@ "execution_count": 8, "id": "eaf84e9c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.571182Z", - "iopub.status.busy": "2023-08-22T06:36:38.571106Z", - "iopub.status.idle": "2023-08-22T06:36:38.572982Z", - "shell.execute_reply": "2023-08-22T06:36:38.572746Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -311,12 +271,7 @@ "execution_count": 9, "id": "d007a49b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.574407Z", - "iopub.status.busy": "2023-08-22T06:36:38.574330Z", - "iopub.status.idle": "2023-08-22T06:36:38.575831Z", - "shell.execute_reply": "2023-08-22T06:36:38.575582Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -339,12 +294,7 @@ "execution_count": 10, "id": "9da64364", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.577295Z", - "iopub.status.busy": "2023-08-22T06:36:38.577214Z", - "iopub.status.idle": "2023-08-22T06:36:38.582891Z", - "shell.execute_reply": "2023-08-22T06:36:38.582646Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -373,12 +323,7 @@ "execution_count": 11, "id": "a2cfe999", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.584411Z", - "iopub.status.busy": "2023-08-22T06:36:38.584330Z", - "iopub.status.idle": "2023-08-22T06:36:38.602242Z", - "shell.execute_reply": "2023-08-22T06:36:38.601986Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -415,12 +360,7 @@ "execution_count": 12, "id": "5c600069", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.603814Z", - "iopub.status.busy": "2023-08-22T06:36:38.603735Z", - "iopub.status.idle": "2023-08-22T06:36:38.605828Z", - "shell.execute_reply": "2023-08-22T06:36:38.605618Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -447,12 +387,7 @@ "execution_count": 13, "id": "6ea4f551", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.607262Z", - "iopub.status.busy": "2023-08-22T06:36:38.607183Z", - "iopub.status.idle": "2023-08-22T06:36:38.611049Z", - "shell.execute_reply": "2023-08-22T06:36:38.610800Z" - } + "execution": {} }, "outputs": [ { @@ -490,12 +425,7 @@ "execution_count": 14, "id": "f1b8b3f5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.612499Z", - "iopub.status.busy": "2023-08-22T06:36:38.612413Z", - "iopub.status.idle": "2023-08-22T06:36:38.614248Z", - "shell.execute_reply": "2023-08-22T06:36:38.614031Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -520,12 +450,7 @@ "execution_count": 15, "id": "50ce4171", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.615637Z", - "iopub.status.busy": "2023-08-22T06:36:38.615534Z", - "iopub.status.idle": "2023-08-22T06:36:38.617783Z", - "shell.execute_reply": "2023-08-22T06:36:38.617551Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -551,12 +476,7 @@ "execution_count": 16, "id": "94c4ab75", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:38.619363Z", - "iopub.status.busy": "2023-08-22T06:36:38.619259Z", - "iopub.status.idle": "2023-08-22T06:36:50.960206Z", - "shell.execute_reply": "2023-08-22T06:36:50.959901Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -585,12 +505,7 @@ "execution_count": 17, "id": "86e45999", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.962061Z", - "iopub.status.busy": "2023-08-22T06:36:50.961947Z", - "iopub.status.idle": "2023-08-22T06:36:50.964427Z", - "shell.execute_reply": "2023-08-22T06:36:50.964181Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -633,12 +548,7 @@ "execution_count": 18, "id": "00ac7606", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.965887Z", - "iopub.status.busy": "2023-08-22T06:36:50.965780Z", - "iopub.status.idle": "2023-08-22T06:36:50.967993Z", - "shell.execute_reply": "2023-08-22T06:36:50.967736Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -698,12 +608,7 @@ "execution_count": 19, "id": "bb7ff7e9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.969396Z", - "iopub.status.busy": "2023-08-22T06:36:50.969288Z", - "iopub.status.idle": "2023-08-22T06:36:50.971584Z", - "shell.execute_reply": "2023-08-22T06:36:50.971363Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -740,12 +645,7 @@ "execution_count": 20, "id": "b60d34e1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.973300Z", - "iopub.status.busy": "2023-08-22T06:36:50.973196Z", - "iopub.status.idle": "2023-08-22T06:36:50.979604Z", - "shell.execute_reply": "2023-08-22T06:36:50.979367Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -814,12 +714,7 @@ "execution_count": 21, "id": "42f63682", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.981129Z", - "iopub.status.busy": "2023-08-22T06:36:50.981043Z", - "iopub.status.idle": "2023-08-22T06:36:50.983074Z", - "shell.execute_reply": "2023-08-22T06:36:50.982803Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -842,12 +737,7 @@ "execution_count": 22, "id": "57fbf564", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.984595Z", - "iopub.status.busy": "2023-08-22T06:36:50.984509Z", - "iopub.status.idle": "2023-08-22T06:36:50.986442Z", - "shell.execute_reply": "2023-08-22T06:36:50.986202Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -884,12 +774,7 @@ "execution_count": 23, "id": "570bdd73", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.987814Z", - "iopub.status.busy": "2023-08-22T06:36:50.987734Z", - "iopub.status.idle": "2023-08-22T06:36:50.989404Z", - "shell.execute_reply": "2023-08-22T06:36:50.989173Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -923,12 +808,7 @@ "execution_count": 24, "id": "c08a4d6d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.990908Z", - "iopub.status.busy": "2023-08-22T06:36:50.990826Z", - "iopub.status.idle": "2023-08-22T06:36:50.992776Z", - "shell.execute_reply": "2023-08-22T06:36:50.992504Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -958,12 +838,7 @@ "execution_count": 25, "id": "aaa1e593", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.994186Z", - "iopub.status.busy": "2023-08-22T06:36:50.994113Z", - "iopub.status.idle": "2023-08-22T06:36:50.996729Z", - "shell.execute_reply": "2023-08-22T06:36:50.996486Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -992,12 +867,7 @@ "execution_count": 26, "id": "1a4e9b3c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:50.998209Z", - "iopub.status.busy": "2023-08-22T06:36:50.998126Z", - "iopub.status.idle": "2023-08-22T06:36:51.000007Z", - "shell.execute_reply": "2023-08-22T06:36:50.999781Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1030,12 +900,7 @@ "execution_count": 27, "id": "2f839fde", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:51.001398Z", - "iopub.status.busy": "2023-08-22T06:36:51.001321Z", - "iopub.status.idle": "2023-08-22T06:36:57.231774Z", - "shell.execute_reply": "2023-08-22T06:36:57.231473Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1824,12 +1689,7 @@ "execution_count": 28, "id": "672b4410", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:57.233763Z", - "iopub.status.busy": "2023-08-22T06:36:57.233589Z", - "iopub.status.idle": "2023-08-22T06:36:58.499876Z", - "shell.execute_reply": "2023-08-22T06:36:58.499595Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1894,12 +1754,7 @@ "execution_count": 29, "id": "8cf6ef60", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.501449Z", - "iopub.status.busy": "2023-08-22T06:36:58.501342Z", - "iopub.status.idle": "2023-08-22T06:36:58.505142Z", - "shell.execute_reply": "2023-08-22T06:36:58.504888Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1920,12 +1775,7 @@ "execution_count": 30, "id": "67ce1e26", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.506556Z", - "iopub.status.busy": "2023-08-22T06:36:58.506485Z", - "iopub.status.idle": "2023-08-22T06:36:58.508767Z", - "shell.execute_reply": "2023-08-22T06:36:58.508514Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1969,12 +1819,7 @@ "execution_count": 31, "id": "deb684d2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.510129Z", - "iopub.status.busy": "2023-08-22T06:36:58.510060Z", - "iopub.status.idle": "2023-08-22T06:36:58.709656Z", - "shell.execute_reply": "2023-08-22T06:36:58.700996Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2022,12 +1867,7 @@ "execution_count": 32, "id": "454033dd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.732799Z", - "iopub.status.busy": "2023-08-22T06:36:58.729950Z", - "iopub.status.idle": "2023-08-22T06:36:58.780581Z", - "shell.execute_reply": "2023-08-22T06:36:58.765673Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2074,12 +1914,7 @@ "execution_count": 33, "id": "71b3d0d0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.807133Z", - "iopub.status.busy": "2023-08-22T06:36:58.800859Z", - "iopub.status.idle": "2023-08-22T06:36:58.836318Z", - "shell.execute_reply": "2023-08-22T06:36:58.822052Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -2113,12 +1948,7 @@ "execution_count": 34, "id": "def8605c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.852033Z", - "iopub.status.busy": "2023-08-22T06:36:58.848358Z", - "iopub.status.idle": "2023-08-22T06:36:58.873333Z", - "shell.execute_reply": "2023-08-22T06:36:58.873077Z" - } + "execution": {} }, "outputs": [ { @@ -2175,12 +2005,7 @@ "execution_count": 35, "id": "8b9e2b8c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.874850Z", - "iopub.status.busy": "2023-08-22T06:36:58.874776Z", - "iopub.status.idle": "2023-08-22T06:36:58.877600Z", - "shell.execute_reply": "2023-08-22T06:36:58.877374Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2205,12 +2030,7 @@ "execution_count": 36, "id": "a4b95dc6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:36:58.879121Z", - "iopub.status.busy": "2023-08-22T06:36:58.879027Z", - "iopub.status.idle": "2023-08-22T06:37:01.493098Z", - "shell.execute_reply": "2023-08-22T06:37:01.492695Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2250,12 +2070,7 @@ "execution_count": 37, "id": "17714c25", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:01.495059Z", - "iopub.status.busy": "2023-08-22T06:37:01.494927Z", - "iopub.status.idle": "2023-08-22T06:37:01.497746Z", - "shell.execute_reply": "2023-08-22T06:37:01.497412Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2297,12 +2112,7 @@ "execution_count": 38, "id": "88a4bf46", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:01.499269Z", - "iopub.status.busy": "2023-08-22T06:37:01.499164Z", - "iopub.status.idle": "2023-08-22T06:37:01.501783Z", - "shell.execute_reply": "2023-08-22T06:37:01.501554Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2323,12 +2133,7 @@ "execution_count": 39, "id": "ea0d9387", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:01.503288Z", - "iopub.status.busy": "2023-08-22T06:37:01.503188Z", - "iopub.status.idle": "2023-08-22T06:37:01.506547Z", - "shell.execute_reply": "2023-08-22T06:37:01.506292Z" - } + "execution": {} }, "outputs": [ { @@ -2361,12 +2166,7 @@ "execution_count": 40, "id": "17c34a29", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:01.507963Z", - "iopub.status.busy": "2023-08-22T06:37:01.507888Z", - "iopub.status.idle": "2023-08-22T06:37:01.511369Z", - "shell.execute_reply": "2023-08-22T06:37:01.511148Z" - } + "execution": {} }, "outputs": [ { @@ -2430,12 +2230,7 @@ "execution_count": 41, "id": "a0608bd1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:01.512793Z", - "iopub.status.busy": "2023-08-22T06:37:01.512716Z", - "iopub.status.idle": "2023-08-22T06:37:01.515484Z", - "shell.execute_reply": "2023-08-22T06:37:01.515264Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2457,12 +2252,7 @@ "execution_count": 42, "id": "cf8e3d9d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:01.516943Z", - "iopub.status.busy": "2023-08-22T06:37:01.516871Z", - "iopub.status.idle": "2023-08-22T06:37:52.281809Z", - "shell.execute_reply": "2023-08-22T06:37:52.281494Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2986,12 +2776,7 @@ "execution_count": 43, "id": "45e03385", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:52.284184Z", - "iopub.status.busy": "2023-08-22T06:37:52.284067Z", - "iopub.status.idle": "2023-08-22T06:37:52.410238Z", - "shell.execute_reply": "2023-08-22T06:37:52.398096Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3032,12 +2817,7 @@ "execution_count": 44, "id": "3a875b78", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:52.428640Z", - "iopub.status.busy": "2023-08-22T06:37:52.428508Z", - "iopub.status.idle": "2023-08-22T06:37:54.669809Z", - "shell.execute_reply": "2023-08-22T06:37:54.669487Z" - } + "execution": {} }, "outputs": [ { @@ -3100,12 +2880,7 @@ "execution_count": 45, "id": "2f035d83", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:54.671428Z", - "iopub.status.busy": "2023-08-22T06:37:54.671322Z", - "iopub.status.idle": "2023-08-22T06:37:54.674968Z", - "shell.execute_reply": "2023-08-22T06:37:54.674733Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -3128,12 +2903,7 @@ "execution_count": 46, "id": "2cd67ad8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:37:54.676335Z", - "iopub.status.busy": "2023-08-22T06:37:54.676230Z", - "iopub.status.idle": "2023-08-22T06:38:31.495310Z", - "shell.execute_reply": "2023-08-22T06:38:31.495001Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3635,12 +3405,7 @@ "execution_count": 47, "id": "3900d16e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:31.497398Z", - "iopub.status.busy": "2023-08-22T06:38:31.497283Z", - "iopub.status.idle": "2023-08-22T06:38:33.741306Z", - "shell.execute_reply": "2023-08-22T06:38:33.740988Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3702,12 +3467,7 @@ "execution_count": 48, "id": "679f2ea5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:33.742928Z", - "iopub.status.busy": "2023-08-22T06:38:33.742820Z", - "iopub.status.idle": "2023-08-22T06:38:33.744700Z", - "shell.execute_reply": "2023-08-22T06:38:33.744472Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -3739,12 +3499,7 @@ "execution_count": 49, "id": "e4af6604", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:33.746175Z", - "iopub.status.busy": "2023-08-22T06:38:33.746084Z", - "iopub.status.idle": "2023-08-22T06:38:35.260793Z", - "shell.execute_reply": "2023-08-22T06:38:35.260496Z" - } + "execution": {} }, "outputs": [ { @@ -3769,12 +3524,7 @@ "execution_count": 50, "id": "2b613ecc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:35.262479Z", - "iopub.status.busy": "2023-08-22T06:38:35.262391Z", - "iopub.status.idle": "2023-08-22T06:38:36.005488Z", - "shell.execute_reply": "2023-08-22T06:38:36.005166Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -3807,12 +3557,7 @@ "execution_count": 51, "id": "4b325cb4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:36.007353Z", - "iopub.status.busy": "2023-08-22T06:38:36.007234Z", - "iopub.status.idle": "2023-08-22T06:38:36.010064Z", - "shell.execute_reply": "2023-08-22T06:38:36.009819Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -3837,12 +3582,7 @@ "execution_count": 52, "id": "cb3d00cb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:36.011610Z", - "iopub.status.busy": "2023-08-22T06:38:36.011502Z", - "iopub.status.idle": "2023-08-22T06:38:38.616192Z", - "shell.execute_reply": "2023-08-22T06:38:38.615836Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3882,12 +3622,7 @@ "execution_count": 53, "id": "60d09656", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:38.618165Z", - "iopub.status.busy": "2023-08-22T06:38:38.618040Z", - "iopub.status.idle": "2023-08-22T06:38:38.959064Z", - "shell.execute_reply": "2023-08-22T06:38:38.950625Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -3938,12 +3673,7 @@ "execution_count": 54, "id": "f823da11", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:38.970097Z", - "iopub.status.busy": "2023-08-22T06:38:38.969929Z", - "iopub.status.idle": "2023-08-22T06:38:38.990599Z", - "shell.execute_reply": "2023-08-22T06:38:38.982099Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -3990,12 +3720,7 @@ "execution_count": 55, "id": "1a172f7e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:39.009337Z", - "iopub.status.busy": "2023-08-22T06:38:39.007564Z", - "iopub.status.idle": "2023-08-22T06:38:39.027768Z", - "shell.execute_reply": "2023-08-22T06:38:39.017688Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -4033,12 +3758,7 @@ "execution_count": 56, "id": "651e62b4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:39.041161Z", - "iopub.status.busy": "2023-08-22T06:38:39.040974Z", - "iopub.status.idle": "2023-08-22T06:38:39.147687Z", - "shell.execute_reply": "2023-08-22T06:38:39.147402Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -4136,12 +3856,7 @@ "execution_count": 57, "id": "63f2650e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:39.149388Z", - "iopub.status.busy": "2023-08-22T06:38:39.149267Z", - "iopub.status.idle": "2023-08-22T06:38:39.152471Z", - "shell.execute_reply": "2023-08-22T06:38:39.152156Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -4157,12 +3872,7 @@ "execution_count": 58, "id": "a3e4bc28", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:38:39.153978Z", - "iopub.status.busy": "2023-08-22T06:38:39.153895Z", - "iopub.status.idle": "2023-08-22T06:41:13.243974Z", - "shell.execute_reply": "2023-08-22T06:41:13.243600Z" - } + "execution": {} }, "outputs": [ { @@ -4670,12 +4380,7 @@ "execution_count": 59, "id": "6b161d93", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:41:13.246757Z", - "iopub.status.busy": "2023-08-22T06:41:13.246644Z", - "iopub.status.idle": "2023-08-22T06:41:13.338213Z", - "shell.execute_reply": "2023-08-22T06:41:13.337367Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -4716,12 +4421,7 @@ "execution_count": 60, "id": "ab8a91dc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:41:13.343721Z", - "iopub.status.busy": "2023-08-22T06:41:13.343026Z", - "iopub.status.idle": "2023-08-22T06:41:15.701168Z", - "shell.execute_reply": "2023-08-22T06:41:15.700777Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -4792,12 +4492,7 @@ "execution_count": 61, "id": "6d9962ae", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:41:15.702808Z", - "iopub.status.busy": "2023-08-22T06:41:15.702702Z", - "iopub.status.idle": "2023-08-22T06:44:01.467525Z", - "shell.execute_reply": "2023-08-22T06:44:01.467145Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5350,12 +5045,7 @@ "execution_count": 62, "id": "a71c9acb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:01.469991Z", - "iopub.status.busy": "2023-08-22T06:44:01.469875Z", - "iopub.status.idle": "2023-08-22T06:44:01.935872Z", - "shell.execute_reply": "2023-08-22T06:44:01.935582Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -5395,12 +5085,7 @@ "execution_count": 63, "id": "4f890244", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:01.937487Z", - "iopub.status.busy": "2023-08-22T06:44:01.937384Z", - "iopub.status.idle": "2023-08-22T06:44:02.437287Z", - "shell.execute_reply": "2023-08-22T06:44:02.436974Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5625,12 +5310,7 @@ "execution_count": 64, "id": "c4be9922", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.438850Z", - "iopub.status.busy": "2023-08-22T06:44:02.438741Z", - "iopub.status.idle": "2023-08-22T06:44:02.441925Z", - "shell.execute_reply": "2023-08-22T06:44:02.441612Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -5841,12 +5521,7 @@ "execution_count": 65, "id": "2dc63d85", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.443425Z", - "iopub.status.busy": "2023-08-22T06:44:02.443344Z", - "iopub.status.idle": "2023-08-22T06:44:02.567717Z", - "shell.execute_reply": "2023-08-22T06:44:02.567412Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -5869,12 +5544,7 @@ "execution_count": 66, "id": "711d5ba7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.569455Z", - "iopub.status.busy": "2023-08-22T06:44:02.569371Z", - "iopub.status.idle": "2023-08-22T06:44:02.571608Z", - "shell.execute_reply": "2023-08-22T06:44:02.571356Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -5895,12 +5565,7 @@ "execution_count": 67, "id": "b22f70d8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.573175Z", - "iopub.status.busy": "2023-08-22T06:44:02.573082Z", - "iopub.status.idle": "2023-08-22T06:44:02.580482Z", - "shell.execute_reply": "2023-08-22T06:44:02.580237Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -5927,12 +5592,7 @@ "execution_count": 68, "id": "b19c6bd1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.581982Z", - "iopub.status.busy": "2023-08-22T06:44:02.581889Z", - "iopub.status.idle": "2023-08-22T06:44:02.593476Z", - "shell.execute_reply": "2023-08-22T06:44:02.593210Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -5999,12 +5659,7 @@ "execution_count": 69, "id": "ba80b615", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.594912Z", - "iopub.status.busy": "2023-08-22T06:44:02.594816Z", - "iopub.status.idle": "2023-08-22T06:44:02.596527Z", - "shell.execute_reply": "2023-08-22T06:44:02.596344Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -6049,12 +5704,7 @@ "execution_count": 70, "id": "ba6d2d2c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.597905Z", - "iopub.status.busy": "2023-08-22T06:44:02.597817Z", - "iopub.status.idle": "2023-08-22T06:44:02.626372Z", - "shell.execute_reply": "2023-08-22T06:44:02.626104Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -6100,12 +5750,7 @@ "execution_count": 71, "id": "93bda908", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.627866Z", - "iopub.status.busy": "2023-08-22T06:44:02.627766Z", - "iopub.status.idle": "2023-08-22T06:44:02.638183Z", - "shell.execute_reply": "2023-08-22T06:44:02.637892Z" - } + "execution": {} }, "outputs": [ { @@ -6141,12 +5786,7 @@ "execution_count": 72, "id": "40943b7d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:02.639670Z", - "iopub.status.busy": "2023-08-22T06:44:02.639577Z", - "iopub.status.idle": "2023-08-22T06:44:03.265220Z", - "shell.execute_reply": "2023-08-22T06:44:03.264917Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6174,12 +5814,7 @@ "execution_count": 73, "id": "2117fd9f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:03.267054Z", - "iopub.status.busy": "2023-08-22T06:44:03.266963Z", - "iopub.status.idle": "2023-08-22T06:44:03.269380Z", - "shell.execute_reply": "2023-08-22T06:44:03.269155Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6217,12 +5852,7 @@ "execution_count": 74, "id": "66d0b710", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:03.270876Z", - "iopub.status.busy": "2023-08-22T06:44:03.270774Z", - "iopub.status.idle": "2023-08-22T06:44:04.097412Z", - "shell.execute_reply": "2023-08-22T06:44:04.097126Z" - } + "execution": {} }, "outputs": [ { @@ -6286,12 +5916,7 @@ "execution_count": 75, "id": "9df8b4cf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:04.101984Z", - "iopub.status.busy": "2023-08-22T06:44:04.101529Z", - "iopub.status.idle": "2023-08-22T06:44:04.111524Z", - "shell.execute_reply": "2023-08-22T06:44:04.110929Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -6316,12 +5941,7 @@ "execution_count": 76, "id": "73684c66", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:04.113582Z", - "iopub.status.busy": "2023-08-22T06:44:04.113437Z", - "iopub.status.idle": "2023-08-22T06:44:22.590794Z", - "shell.execute_reply": "2023-08-22T06:44:22.590448Z" - } + "execution": {} }, "outputs": [ { @@ -6831,12 +6451,7 @@ "execution_count": 77, "id": "01c6e5ff", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:22.593099Z", - "iopub.status.busy": "2023-08-22T06:44:22.592980Z", - "iopub.status.idle": "2023-08-22T06:44:24.381045Z", - "shell.execute_reply": "2023-08-22T06:44:24.380564Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -6899,12 +6514,7 @@ "execution_count": 78, "id": "4d5b9d1d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:24.382775Z", - "iopub.status.busy": "2023-08-22T06:44:24.382660Z", - "iopub.status.idle": "2023-08-22T06:44:24.491042Z", - "shell.execute_reply": "2023-08-22T06:44:24.490723Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -6930,12 +6540,7 @@ "execution_count": 79, "id": "e2a88e57", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:24.492855Z", - "iopub.status.busy": "2023-08-22T06:44:24.492752Z", - "iopub.status.idle": "2023-08-22T06:44:24.497193Z", - "shell.execute_reply": "2023-08-22T06:44:24.496916Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6961,12 +6566,7 @@ "execution_count": 80, "id": "9a3cf7a3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:24.498888Z", - "iopub.status.busy": "2023-08-22T06:44:24.498790Z", - "iopub.status.idle": "2023-08-22T06:44:24.500775Z", - "shell.execute_reply": "2023-08-22T06:44:24.500516Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -6991,12 +6591,7 @@ "execution_count": 81, "id": "b46f02c2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:24.502208Z", - "iopub.status.busy": "2023-08-22T06:44:24.502114Z", - "iopub.status.idle": "2023-08-22T06:44:43.491232Z", - "shell.execute_reply": "2023-08-22T06:44:43.490911Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -7024,12 +6619,7 @@ "execution_count": 82, "id": "e5fb6afa", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:43.492990Z", - "iopub.status.busy": "2023-08-22T06:44:43.492881Z", - "iopub.status.idle": "2023-08-22T06:44:43.495136Z", - "shell.execute_reply": "2023-08-22T06:44:43.494898Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -7052,12 +6642,7 @@ "execution_count": 83, "id": "cad28f1a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:43.496615Z", - "iopub.status.busy": "2023-08-22T06:44:43.496512Z", - "iopub.status.idle": "2023-08-22T06:44:43.817649Z", - "shell.execute_reply": "2023-08-22T06:44:43.804167Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -7101,12 +6686,7 @@ "execution_count": 84, "id": "a66ecdd8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:43.829522Z", - "iopub.status.busy": "2023-08-22T06:44:43.829394Z", - "iopub.status.idle": "2023-08-22T06:44:43.970259Z", - "shell.execute_reply": "2023-08-22T06:44:43.969880Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7154,12 +6734,7 @@ "execution_count": 85, "id": "62440c1c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:43.972104Z", - "iopub.status.busy": "2023-08-22T06:44:43.971981Z", - "iopub.status.idle": "2023-08-22T06:44:43.973750Z", - "shell.execute_reply": "2023-08-22T06:44:43.973468Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -7206,12 +6781,7 @@ "execution_count": 86, "id": "c73d6e28", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:43.975482Z", - "iopub.status.busy": "2023-08-22T06:44:43.975361Z", - "iopub.status.idle": "2023-08-22T06:44:43.977735Z", - "shell.execute_reply": "2023-08-22T06:44:43.977461Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -7256,12 +6826,7 @@ "execution_count": 87, "id": "cc9bbd00", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:43.979333Z", - "iopub.status.busy": "2023-08-22T06:44:43.979219Z", - "iopub.status.idle": "2023-08-22T06:44:43.981720Z", - "shell.execute_reply": "2023-08-22T06:44:43.981436Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -7293,12 +6858,7 @@ "execution_count": 88, "id": "5c9ffb46", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:43.983706Z", - "iopub.status.busy": "2023-08-22T06:44:43.983605Z", - "iopub.status.idle": "2023-08-22T06:44:44.006133Z", - "shell.execute_reply": "2023-08-22T06:44:44.005857Z" - } + "execution": {} }, "outputs": [ { @@ -7352,12 +6912,7 @@ "execution_count": 89, "id": "a2d6ddfd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:44.007842Z", - "iopub.status.busy": "2023-08-22T06:44:44.007718Z", - "iopub.status.idle": "2023-08-22T06:44:44.010586Z", - "shell.execute_reply": "2023-08-22T06:44:44.010323Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -7370,12 +6925,7 @@ "execution_count": 90, "id": "1d76f970", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:44:44.012129Z", - "iopub.status.busy": "2023-08-22T06:44:44.012007Z", - "iopub.status.idle": "2023-08-22T06:57:52.592058Z", - "shell.execute_reply": "2023-08-22T06:57:52.591679Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -7738,12 +7288,7 @@ "execution_count": 91, "id": "d8a60d35", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:52.595337Z", - "iopub.status.busy": "2023-08-22T06:57:52.595214Z", - "iopub.status.idle": "2023-08-22T06:57:55.788029Z", - "shell.execute_reply": "2023-08-22T06:57:55.787703Z" - } + "execution": {} }, "outputs": [ { @@ -7800,12 +7345,7 @@ "execution_count": 92, "id": "65d7276c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:55.789720Z", - "iopub.status.busy": "2023-08-22T06:57:55.789606Z", - "iopub.status.idle": "2023-08-22T06:57:55.884354Z", - "shell.execute_reply": "2023-08-22T06:57:55.882420Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -7847,12 +7387,7 @@ "execution_count": 93, "id": "c6f2d6c4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:55.889361Z", - "iopub.status.busy": "2023-08-22T06:57:55.889040Z", - "iopub.status.idle": "2023-08-22T06:57:55.894226Z", - "shell.execute_reply": "2023-08-22T06:57:55.893514Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -7881,12 +7416,7 @@ "execution_count": 94, "id": "f3e17682", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:55.899236Z", - "iopub.status.busy": "2023-08-22T06:57:55.898584Z", - "iopub.status.idle": "2023-08-22T06:57:55.919645Z", - "shell.execute_reply": "2023-08-22T06:57:55.918698Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -7913,12 +7443,7 @@ "execution_count": 95, "id": "78707eda", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:55.924653Z", - "iopub.status.busy": "2023-08-22T06:57:55.924212Z", - "iopub.status.idle": "2023-08-22T06:57:55.941488Z", - "shell.execute_reply": "2023-08-22T06:57:55.940914Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -7945,12 +7470,7 @@ "execution_count": 96, "id": "4d894824", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:55.946619Z", - "iopub.status.busy": "2023-08-22T06:57:55.946232Z", - "iopub.status.idle": "2023-08-22T06:57:55.957091Z", - "shell.execute_reply": "2023-08-22T06:57:55.956345Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -7989,12 +7509,7 @@ "execution_count": 97, "id": "4d7f5ce0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:55.961811Z", - "iopub.status.busy": "2023-08-22T06:57:55.961399Z", - "iopub.status.idle": "2023-08-22T06:57:56.102250Z", - "shell.execute_reply": "2023-08-22T06:57:56.100744Z" - } + "execution": {} }, "outputs": [ { @@ -8029,12 +7544,7 @@ "execution_count": 98, "id": "a6b371bb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.107157Z", - "iopub.status.busy": "2023-08-22T06:57:56.106829Z", - "iopub.status.idle": "2023-08-22T06:57:56.120860Z", - "shell.execute_reply": "2023-08-22T06:57:56.120304Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -8059,12 +7569,7 @@ "execution_count": 99, "id": "a2a8cc85", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.124686Z", - "iopub.status.busy": "2023-08-22T06:57:56.124536Z", - "iopub.status.idle": "2023-08-22T06:57:56.148874Z", - "shell.execute_reply": "2023-08-22T06:57:56.148350Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8117,12 +7622,7 @@ "execution_count": 100, "id": "8ee6e6a3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.154237Z", - "iopub.status.busy": "2023-08-22T06:57:56.153271Z", - "iopub.status.idle": "2023-08-22T06:57:56.164612Z", - "shell.execute_reply": "2023-08-22T06:57:56.162355Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8163,12 +7663,7 @@ "execution_count": 101, "id": "d35ceb54", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.168885Z", - "iopub.status.busy": "2023-08-22T06:57:56.168313Z", - "iopub.status.idle": "2023-08-22T06:57:56.177028Z", - "shell.execute_reply": "2023-08-22T06:57:56.174770Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8207,12 +7702,7 @@ "execution_count": 102, "id": "9e3dc6d5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.180875Z", - "iopub.status.busy": "2023-08-22T06:57:56.180571Z", - "iopub.status.idle": "2023-08-22T06:57:56.188877Z", - "shell.execute_reply": "2023-08-22T06:57:56.188074Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -8251,12 +7741,7 @@ "execution_count": 103, "id": "df5e5ab6", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.192921Z", - "iopub.status.busy": "2023-08-22T06:57:56.192198Z", - "iopub.status.idle": "2023-08-22T06:57:56.205860Z", - "shell.execute_reply": "2023-08-22T06:57:56.203593Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -8281,12 +7766,7 @@ "execution_count": 104, "id": "d7f49bec", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.209732Z", - "iopub.status.busy": "2023-08-22T06:57:56.209439Z", - "iopub.status.idle": "2023-08-22T06:57:56.224094Z", - "shell.execute_reply": "2023-08-22T06:57:56.221572Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -8340,12 +7820,7 @@ "execution_count": 105, "id": "ea7ce0f4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.228022Z", - "iopub.status.busy": "2023-08-22T06:57:56.227434Z", - "iopub.status.idle": "2023-08-22T06:57:56.236333Z", - "shell.execute_reply": "2023-08-22T06:57:56.234101Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -8370,12 +7845,7 @@ "execution_count": 106, "id": "ccd77738", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:56.242815Z", - "iopub.status.busy": "2023-08-22T06:57:56.242259Z", - "iopub.status.idle": "2023-08-22T06:57:57.426177Z", - "shell.execute_reply": "2023-08-22T06:57:57.425792Z" - } + "execution": {} }, "outputs": [ { @@ -8411,12 +7881,7 @@ "execution_count": 107, "id": "96e04e3f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:57.428165Z", - "iopub.status.busy": "2023-08-22T06:57:57.428026Z", - "iopub.status.idle": "2023-08-22T06:57:57.431868Z", - "shell.execute_reply": "2023-08-22T06:57:57.431594Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -8441,12 +7906,7 @@ "execution_count": 108, "id": "fc6ba2ca", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T06:57:57.433417Z", - "iopub.status.busy": "2023-08-22T06:57:57.433307Z", - "iopub.status.idle": "2023-08-22T07:00:40.275665Z", - "shell.execute_reply": "2023-08-22T07:00:40.275338Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -11373,12 +10833,7 @@ "execution_count": 109, "id": "c60876d7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:40.277902Z", - "iopub.status.busy": "2023-08-22T07:00:40.277770Z", - "iopub.status.idle": "2023-08-22T07:00:40.284106Z", - "shell.execute_reply": "2023-08-22T07:00:40.283818Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -11404,12 +10859,7 @@ "execution_count": 110, "id": "282f7e1c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:40.285727Z", - "iopub.status.busy": "2023-08-22T07:00:40.285637Z", - "iopub.status.idle": "2023-08-22T07:00:40.287708Z", - "shell.execute_reply": "2023-08-22T07:00:40.287434Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -11433,12 +10883,7 @@ "execution_count": 111, "id": "038fea74", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:40.289256Z", - "iopub.status.busy": "2023-08-22T07:00:40.289169Z", - "iopub.status.idle": "2023-08-22T07:00:40.291814Z", - "shell.execute_reply": "2023-08-22T07:00:40.291551Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -11459,12 +10904,7 @@ "execution_count": 112, "id": "68d309b2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:40.293426Z", - "iopub.status.busy": "2023-08-22T07:00:40.293319Z", - "iopub.status.idle": "2023-08-22T07:00:40.296658Z", - "shell.execute_reply": "2023-08-22T07:00:40.296393Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -11490,12 +10930,7 @@ "execution_count": 113, "id": "9fc113a1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:40.298197Z", - "iopub.status.busy": "2023-08-22T07:00:40.298117Z", - "iopub.status.idle": "2023-08-22T07:00:54.837234Z", - "shell.execute_reply": "2023-08-22T07:00:54.836928Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ diff --git a/Ch11-surv-lab.ipynb b/Ch11-surv-lab.ipynb index afc93f4..70c27d5 100644 --- a/Ch11-surv-lab.ipynb +++ b/Ch11-surv-lab.ipynb @@ -33,12 +33,7 @@ "execution_count": 1, "id": "d2d71add", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:50.337731Z", - "iopub.status.busy": "2023-08-22T07:00:50.337629Z", - "iopub.status.idle": "2023-08-22T07:00:51.436060Z", - "shell.execute_reply": "2023-08-22T07:00:51.435683Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -63,12 +58,7 @@ "execution_count": 2, "id": "994efc94", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.438092Z", - "iopub.status.busy": "2023-08-22T07:00:51.437918Z", - "iopub.status.idle": "2023-08-22T07:00:51.535762Z", - "shell.execute_reply": "2023-08-22T07:00:51.535441Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -96,12 +86,7 @@ "execution_count": 3, "id": "9d41ddea", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.537713Z", - "iopub.status.busy": "2023-08-22T07:00:51.537591Z", - "iopub.status.idle": "2023-08-22T07:00:51.544098Z", - "shell.execute_reply": "2023-08-22T07:00:51.543823Z" - } + "execution": {} }, "outputs": [ { @@ -134,12 +119,7 @@ "execution_count": 4, "id": "2bece782", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.545870Z", - "iopub.status.busy": "2023-08-22T07:00:51.545663Z", - "iopub.status.idle": "2023-08-22T07:00:51.548757Z", - "shell.execute_reply": "2023-08-22T07:00:51.548485Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -165,12 +145,7 @@ "execution_count": 5, "id": "9ca465e5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.550308Z", - "iopub.status.busy": "2023-08-22T07:00:51.550187Z", - "iopub.status.idle": "2023-08-22T07:00:51.553059Z", - "shell.execute_reply": "2023-08-22T07:00:51.552776Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -198,12 +173,7 @@ "execution_count": 6, "id": "33bc4d3c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.554804Z", - "iopub.status.busy": "2023-08-22T07:00:51.554671Z", - "iopub.status.idle": "2023-08-22T07:00:51.557626Z", - "shell.execute_reply": "2023-08-22T07:00:51.557320Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -254,12 +224,7 @@ "execution_count": 7, "id": "0b6dba70", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.559244Z", - "iopub.status.busy": "2023-08-22T07:00:51.559137Z", - "iopub.status.idle": "2023-08-22T07:00:51.665338Z", - "shell.execute_reply": "2023-08-22T07:00:51.665014Z" - } + "execution": {} }, "outputs": [ { @@ -320,12 +285,7 @@ "execution_count": 8, "id": "9e6f2e70", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.667255Z", - "iopub.status.busy": "2023-08-22T07:00:51.667124Z", - "iopub.status.idle": "2023-08-22T07:00:51.776746Z", - "shell.execute_reply": "2023-08-22T07:00:51.776283Z" - } + "execution": {} }, "outputs": [ { @@ -365,12 +325,7 @@ "execution_count": 9, "id": "c135f7aa", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.778810Z", - "iopub.status.busy": "2023-08-22T07:00:51.778673Z", - "iopub.status.idle": "2023-08-22T07:00:51.828367Z", - "shell.execute_reply": "2023-08-22T07:00:51.827978Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -478,12 +433,7 @@ "execution_count": 10, "id": "5f9303dd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.830679Z", - "iopub.status.busy": "2023-08-22T07:00:51.830375Z", - "iopub.status.idle": "2023-08-22T07:00:51.855011Z", - "shell.execute_reply": "2023-08-22T07:00:51.854602Z" - } + "execution": {} }, "outputs": [ { @@ -571,12 +521,7 @@ "execution_count": 11, "id": "bcc8470c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.857128Z", - "iopub.status.busy": "2023-08-22T07:00:51.856972Z", - "iopub.status.idle": "2023-08-22T07:00:51.862953Z", - "shell.execute_reply": "2023-08-22T07:00:51.862477Z" - } + "execution": {} }, "outputs": [ { @@ -677,12 +622,7 @@ "execution_count": 12, "id": "c26a3499", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.865156Z", - "iopub.status.busy": "2023-08-22T07:00:51.864917Z", - "iopub.status.idle": "2023-08-22T07:00:51.902771Z", - "shell.execute_reply": "2023-08-22T07:00:51.902284Z" - } + "execution": {} }, "outputs": [ { @@ -825,12 +765,7 @@ "execution_count": 13, "id": "8d999f26", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.904821Z", - "iopub.status.busy": "2023-08-22T07:00:51.904683Z", - "iopub.status.idle": "2023-08-22T07:00:51.908678Z", - "shell.execute_reply": "2023-08-22T07:00:51.908230Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -858,12 +793,7 @@ "execution_count": 14, "id": "a1f6b355", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.910559Z", - "iopub.status.busy": "2023-08-22T07:00:51.910451Z", - "iopub.status.idle": "2023-08-22T07:00:51.916316Z", - "shell.execute_reply": "2023-08-22T07:00:51.915990Z" - } + "execution": {} }, "outputs": [ { @@ -986,12 +916,7 @@ "execution_count": 15, "id": "a1a9d5b3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.918281Z", - "iopub.status.busy": "2023-08-22T07:00:51.918144Z", - "iopub.status.idle": "2023-08-22T07:00:51.927845Z", - "shell.execute_reply": "2023-08-22T07:00:51.927490Z" - } + "execution": {} }, "outputs": [ { @@ -1128,12 +1053,7 @@ "execution_count": 16, "id": "1a18b56a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.929658Z", - "iopub.status.busy": "2023-08-22T07:00:51.929522Z", - "iopub.status.idle": "2023-08-22T07:00:51.936112Z", - "shell.execute_reply": "2023-08-22T07:00:51.935793Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1289,12 +1209,7 @@ "execution_count": 17, "id": "ff3de29c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:51.937659Z", - "iopub.status.busy": "2023-08-22T07:00:51.937539Z", - "iopub.status.idle": "2023-08-22T07:00:52.033442Z", - "shell.execute_reply": "2023-08-22T07:00:52.032997Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1332,12 +1247,7 @@ "execution_count": 18, "id": "cd9060c1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.035515Z", - "iopub.status.busy": "2023-08-22T07:00:52.035365Z", - "iopub.status.idle": "2023-08-22T07:00:52.186180Z", - "shell.execute_reply": "2023-08-22T07:00:52.185871Z" - } + "execution": {} }, "outputs": [ { @@ -1377,12 +1287,7 @@ "execution_count": 19, "id": "6af7106e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.188568Z", - "iopub.status.busy": "2023-08-22T07:00:52.188400Z", - "iopub.status.idle": "2023-08-22T07:00:52.221771Z", - "shell.execute_reply": "2023-08-22T07:00:52.221413Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1466,12 +1371,7 @@ "execution_count": 20, "id": "b6ebefa7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.223735Z", - "iopub.status.busy": "2023-08-22T07:00:52.223584Z", - "iopub.status.idle": "2023-08-22T07:00:52.263558Z", - "shell.execute_reply": "2023-08-22T07:00:52.263236Z" - } + "execution": {} }, "outputs": [ { @@ -1610,12 +1510,7 @@ "execution_count": 21, "id": "098f42ea", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.265430Z", - "iopub.status.busy": "2023-08-22T07:00:52.265309Z", - "iopub.status.idle": "2023-08-22T07:00:52.269361Z", - "shell.execute_reply": "2023-08-22T07:00:52.268968Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1648,12 +1543,7 @@ "execution_count": 22, "id": "26d5d0d0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.271325Z", - "iopub.status.busy": "2023-08-22T07:00:52.271227Z", - "iopub.status.idle": "2023-08-22T07:00:52.280726Z", - "shell.execute_reply": "2023-08-22T07:00:52.280283Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1680,12 +1570,7 @@ "execution_count": 23, "id": "77500663", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.282793Z", - "iopub.status.busy": "2023-08-22T07:00:52.282661Z", - "iopub.status.idle": "2023-08-22T07:00:52.287078Z", - "shell.execute_reply": "2023-08-22T07:00:52.286749Z" - } + "execution": {} }, "outputs": [ { @@ -1792,12 +1677,7 @@ "execution_count": 24, "id": "74324a56", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.289254Z", - "iopub.status.busy": "2023-08-22T07:00:52.289116Z", - "iopub.status.idle": "2023-08-22T07:00:52.327077Z", - "shell.execute_reply": "2023-08-22T07:00:52.313280Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1839,12 +1719,7 @@ "execution_count": 25, "id": "d4be10c2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.360673Z", - "iopub.status.busy": "2023-08-22T07:00:52.360466Z", - "iopub.status.idle": "2023-08-22T07:00:52.386485Z", - "shell.execute_reply": "2023-08-22T07:00:52.383597Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1870,12 +1745,7 @@ "execution_count": 26, "id": "c98d396f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.411166Z", - "iopub.status.busy": "2023-08-22T07:00:52.410817Z", - "iopub.status.idle": "2023-08-22T07:00:52.535664Z", - "shell.execute_reply": "2023-08-22T07:00:52.534128Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1899,12 +1769,7 @@ "execution_count": 27, "id": "caf627bc", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.538055Z", - "iopub.status.busy": "2023-08-22T07:00:52.537880Z", - "iopub.status.idle": "2023-08-22T07:00:52.547315Z", - "shell.execute_reply": "2023-08-22T07:00:52.546097Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2007,12 +1872,7 @@ "execution_count": 28, "id": "e63242f9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.553567Z", - "iopub.status.busy": "2023-08-22T07:00:52.553399Z", - "iopub.status.idle": "2023-08-22T07:00:52.556617Z", - "shell.execute_reply": "2023-08-22T07:00:52.556076Z" - } + "execution": {} }, "outputs": [ { @@ -2043,12 +1903,7 @@ "execution_count": 29, "id": "338db71d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.558893Z", - "iopub.status.busy": "2023-08-22T07:00:52.558778Z", - "iopub.status.idle": "2023-08-22T07:00:52.706054Z", - "shell.execute_reply": "2023-08-22T07:00:52.705654Z" - } + "execution": {} }, "outputs": [ { @@ -2095,12 +1950,7 @@ "execution_count": 30, "id": "c1db6e15", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.707873Z", - "iopub.status.busy": "2023-08-22T07:00:52.707772Z", - "iopub.status.idle": "2023-08-22T07:00:52.850431Z", - "shell.execute_reply": "2023-08-22T07:00:52.849948Z" - } + "execution": {} }, "outputs": [ { @@ -2151,12 +2001,7 @@ "execution_count": 31, "id": "02ea4212", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.852333Z", - "iopub.status.busy": "2023-08-22T07:00:52.852184Z", - "iopub.status.idle": "2023-08-22T07:00:52.869743Z", - "shell.execute_reply": "2023-08-22T07:00:52.869438Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2258,12 +2103,7 @@ "execution_count": 32, "id": "0ac610d5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.871905Z", - "iopub.status.busy": "2023-08-22T07:00:52.871758Z", - "iopub.status.idle": "2023-08-22T07:00:52.889214Z", - "shell.execute_reply": "2023-08-22T07:00:52.888915Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2368,12 +2208,7 @@ "execution_count": 33, "id": "107cedad", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:52.891264Z", - "iopub.status.busy": "2023-08-22T07:00:52.891105Z", - "iopub.status.idle": "2023-08-22T07:00:52.999600Z", - "shell.execute_reply": "2023-08-22T07:00:52.999278Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2473,12 +2308,7 @@ "execution_count": 34, "id": "334eb331", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:53.001288Z", - "iopub.status.busy": "2023-08-22T07:00:53.001160Z", - "iopub.status.idle": "2023-08-22T07:00:53.112657Z", - "shell.execute_reply": "2023-08-22T07:00:53.112061Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2581,12 +2411,7 @@ "execution_count": 35, "id": "421811c5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:53.115234Z", - "iopub.status.busy": "2023-08-22T07:00:53.114902Z", - "iopub.status.idle": "2023-08-22T07:00:53.349069Z", - "shell.execute_reply": "2023-08-22T07:00:53.326280Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ diff --git a/Ch12-unsup-lab.ipynb b/Ch12-unsup-lab.ipynb index d9a9a1e..db5293d 100644 --- a/Ch12-unsup-lab.ipynb +++ b/Ch12-unsup-lab.ipynb @@ -21,12 +21,7 @@ "execution_count": 1, "id": "6d5ba583", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:56.821728Z", - "iopub.status.busy": "2023-08-22T07:00:56.821428Z", - "iopub.status.idle": "2023-08-22T07:00:57.569366Z", - "shell.execute_reply": "2023-08-22T07:00:57.569068Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -54,12 +49,7 @@ "execution_count": 2, "id": "64c83257", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:57.571332Z", - "iopub.status.busy": "2023-08-22T07:00:57.571181Z", - "iopub.status.idle": "2023-08-22T07:00:59.834786Z", - "shell.execute_reply": "2023-08-22T07:00:59.834273Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -91,12 +81,7 @@ "execution_count": 3, "id": "04ec4481", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:00:59.837457Z", - "iopub.status.busy": "2023-08-22T07:00:59.837321Z", - "iopub.status.idle": "2023-08-22T07:01:01.128712Z", - "shell.execute_reply": "2023-08-22T07:01:01.128206Z" - } + "execution": {} }, "outputs": [ { @@ -558,12 +543,7 @@ "execution_count": 4, "id": "1b66036a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.131204Z", - "iopub.status.busy": "2023-08-22T07:01:01.130984Z", - "iopub.status.idle": "2023-08-22T07:01:01.135700Z", - "shell.execute_reply": "2023-08-22T07:01:01.134242Z" - } + "execution": {} }, "outputs": [ { @@ -594,12 +574,7 @@ "execution_count": 5, "id": "52e900fd", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.141963Z", - "iopub.status.busy": "2023-08-22T07:01:01.141493Z", - "iopub.status.idle": "2023-08-22T07:01:01.148106Z", - "shell.execute_reply": "2023-08-22T07:01:01.147604Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -637,12 +612,7 @@ "execution_count": 6, "id": "68684f78", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.156074Z", - "iopub.status.busy": "2023-08-22T07:01:01.154480Z", - "iopub.status.idle": "2023-08-22T07:01:01.168063Z", - "shell.execute_reply": "2023-08-22T07:01:01.167370Z" - } + "execution": {} }, "outputs": [ { @@ -691,12 +661,7 @@ "execution_count": 7, "id": "d2b7caf9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.171694Z", - "iopub.status.busy": "2023-08-22T07:01:01.171419Z", - "iopub.status.idle": "2023-08-22T07:01:01.175704Z", - "shell.execute_reply": "2023-08-22T07:01:01.174949Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -721,12 +686,7 @@ "execution_count": 8, "id": "de8f57fa", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.179112Z", - "iopub.status.busy": "2023-08-22T07:01:01.178950Z", - "iopub.status.idle": "2023-08-22T07:01:01.181409Z", - "shell.execute_reply": "2023-08-22T07:01:01.180781Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -750,12 +710,7 @@ "execution_count": 9, "id": "26c45f1e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.184888Z", - "iopub.status.busy": "2023-08-22T07:01:01.184710Z", - "iopub.status.idle": "2023-08-22T07:01:01.188530Z", - "shell.execute_reply": "2023-08-22T07:01:01.188252Z" - } + "execution": {} }, "outputs": [ { @@ -791,12 +746,7 @@ "execution_count": 10, "id": "3097e99d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.190309Z", - "iopub.status.busy": "2023-08-22T07:01:01.190146Z", - "iopub.status.idle": "2023-08-22T07:01:01.193028Z", - "shell.execute_reply": "2023-08-22T07:01:01.192570Z" - } + "execution": {} }, "outputs": [ { @@ -828,12 +778,7 @@ "execution_count": 11, "id": "c071a242", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.196049Z", - "iopub.status.busy": "2023-08-22T07:01:01.195747Z", - "iopub.status.idle": "2023-08-22T07:01:01.198277Z", - "shell.execute_reply": "2023-08-22T07:01:01.197846Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -857,12 +802,7 @@ "execution_count": 12, "id": "c9bcab06", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.200305Z", - "iopub.status.busy": "2023-08-22T07:01:01.200177Z", - "iopub.status.idle": "2023-08-22T07:01:01.202888Z", - "shell.execute_reply": "2023-08-22T07:01:01.202483Z" - } + "execution": {} }, "outputs": [ { @@ -900,12 +840,7 @@ "execution_count": 13, "id": "7375ab13", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.204941Z", - "iopub.status.busy": "2023-08-22T07:01:01.204821Z", - "iopub.status.idle": "2023-08-22T07:01:01.305551Z", - "shell.execute_reply": "2023-08-22T07:01:01.304502Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -950,12 +885,7 @@ "execution_count": 14, "id": "4c1988de", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.308684Z", - "iopub.status.busy": "2023-08-22T07:01:01.308547Z", - "iopub.status.idle": "2023-08-22T07:01:01.398261Z", - "shell.execute_reply": "2023-08-22T07:01:01.397687Z" - } + "execution": {} }, "outputs": [ { @@ -997,12 +927,7 @@ "execution_count": 15, "id": "965c6320", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.400814Z", - "iopub.status.busy": "2023-08-22T07:01:01.400674Z", - "iopub.status.idle": "2023-08-22T07:01:01.404022Z", - "shell.execute_reply": "2023-08-22T07:01:01.403489Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1035,12 +960,7 @@ "execution_count": 16, "id": "cd5e1663", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.405922Z", - "iopub.status.busy": "2023-08-22T07:01:01.405818Z", - "iopub.status.idle": "2023-08-22T07:01:01.409000Z", - "shell.execute_reply": "2023-08-22T07:01:01.408413Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1073,12 +993,7 @@ "execution_count": 17, "id": "e711d1be", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.410782Z", - "iopub.status.busy": "2023-08-22T07:01:01.410676Z", - "iopub.status.idle": "2023-08-22T07:01:01.413542Z", - "shell.execute_reply": "2023-08-22T07:01:01.413036Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1114,12 +1029,7 @@ "execution_count": 18, "id": "e122eb41", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.415587Z", - "iopub.status.busy": "2023-08-22T07:01:01.415405Z", - "iopub.status.idle": "2023-08-22T07:01:01.537691Z", - "shell.execute_reply": "2023-08-22T07:01:01.537297Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1150,12 +1060,7 @@ "execution_count": 19, "id": "bef47d90", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.540279Z", - "iopub.status.busy": "2023-08-22T07:01:01.539986Z", - "iopub.status.idle": "2023-08-22T07:01:01.632911Z", - "shell.execute_reply": "2023-08-22T07:01:01.632328Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1198,12 +1103,7 @@ "execution_count": 20, "id": "f3300d9e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.635302Z", - "iopub.status.busy": "2023-08-22T07:01:01.634975Z", - "iopub.status.idle": "2023-08-22T07:01:01.638324Z", - "shell.execute_reply": "2023-08-22T07:01:01.637831Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1247,12 +1147,7 @@ "execution_count": 21, "id": "20e6009f", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.640494Z", - "iopub.status.busy": "2023-08-22T07:01:01.640389Z", - "iopub.status.idle": "2023-08-22T07:01:01.643841Z", - "shell.execute_reply": "2023-08-22T07:01:01.643438Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1288,12 +1183,7 @@ "execution_count": 22, "id": "7d9937cf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.646567Z", - "iopub.status.busy": "2023-08-22T07:01:01.646396Z", - "iopub.status.idle": "2023-08-22T07:01:01.649238Z", - "shell.execute_reply": "2023-08-22T07:01:01.648799Z" - } + "execution": {} }, "outputs": [ { @@ -1319,12 +1209,7 @@ "execution_count": 23, "id": "e58f83a3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.651066Z", - "iopub.status.busy": "2023-08-22T07:01:01.650908Z", - "iopub.status.idle": "2023-08-22T07:01:01.653787Z", - "shell.execute_reply": "2023-08-22T07:01:01.653263Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1359,12 +1244,7 @@ "execution_count": 24, "id": "5c4f9b34", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.656687Z", - "iopub.status.busy": "2023-08-22T07:01:01.656390Z", - "iopub.status.idle": "2023-08-22T07:01:01.659789Z", - "shell.execute_reply": "2023-08-22T07:01:01.659269Z" - } + "execution": {} }, "outputs": [ { @@ -1389,12 +1269,7 @@ "execution_count": 25, "id": "0ce84f1b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.661783Z", - "iopub.status.busy": "2023-08-22T07:01:01.661671Z", - "iopub.status.idle": "2023-08-22T07:01:01.664431Z", - "shell.execute_reply": "2023-08-22T07:01:01.664078Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1434,12 +1309,7 @@ "execution_count": 26, "id": "cd8b4bed", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.666675Z", - "iopub.status.busy": "2023-08-22T07:01:01.666540Z", - "iopub.status.idle": "2023-08-22T07:01:01.669352Z", - "shell.execute_reply": "2023-08-22T07:01:01.668883Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1474,12 +1344,7 @@ "execution_count": 27, "id": "7f3bc8f9", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.671494Z", - "iopub.status.busy": "2023-08-22T07:01:01.671325Z", - "iopub.status.idle": "2023-08-22T07:01:01.673688Z", - "shell.execute_reply": "2023-08-22T07:01:01.673299Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1507,12 +1372,7 @@ "execution_count": 28, "id": "771a46a7", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.675631Z", - "iopub.status.busy": "2023-08-22T07:01:01.675424Z", - "iopub.status.idle": "2023-08-22T07:01:01.678059Z", - "shell.execute_reply": "2023-08-22T07:01:01.677393Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1535,12 +1395,7 @@ "execution_count": 29, "id": "1416f048", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.680424Z", - "iopub.status.busy": "2023-08-22T07:01:01.680179Z", - "iopub.status.idle": "2023-08-22T07:01:01.682713Z", - "shell.execute_reply": "2023-08-22T07:01:01.682431Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1575,12 +1430,7 @@ "execution_count": 30, "id": "9eff34aa", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.684370Z", - "iopub.status.busy": "2023-08-22T07:01:01.684237Z", - "iopub.status.idle": "2023-08-22T07:01:01.688888Z", - "shell.execute_reply": "2023-08-22T07:01:01.688576Z" - } + "execution": {} }, "outputs": [ { @@ -1630,12 +1480,7 @@ "execution_count": 31, "id": "7815b948", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.690940Z", - "iopub.status.busy": "2023-08-22T07:01:01.690758Z", - "iopub.status.idle": "2023-08-22T07:01:01.694473Z", - "shell.execute_reply": "2023-08-22T07:01:01.694113Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1688,12 +1533,7 @@ "execution_count": 32, "id": "f63cf4b8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.697098Z", - "iopub.status.busy": "2023-08-22T07:01:01.696989Z", - "iopub.status.idle": "2023-08-22T07:01:01.699944Z", - "shell.execute_reply": "2023-08-22T07:01:01.699302Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1717,12 +1557,7 @@ "execution_count": 33, "id": "f973c2d4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:01.701419Z", - "iopub.status.busy": "2023-08-22T07:01:01.701312Z", - "iopub.status.idle": "2023-08-22T07:01:02.037824Z", - "shell.execute_reply": "2023-08-22T07:01:02.037051Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -1745,12 +1580,7 @@ "execution_count": 34, "id": "e980954b", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.040614Z", - "iopub.status.busy": "2023-08-22T07:01:02.040437Z", - "iopub.status.idle": "2023-08-22T07:01:02.044054Z", - "shell.execute_reply": "2023-08-22T07:01:02.043563Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1787,12 +1617,7 @@ "execution_count": 35, "id": "a94d452c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.046357Z", - "iopub.status.busy": "2023-08-22T07:01:02.046208Z", - "iopub.status.idle": "2023-08-22T07:01:02.149266Z", - "shell.execute_reply": "2023-08-22T07:01:02.148849Z" - } + "execution": {} }, "outputs": [ { @@ -1834,12 +1659,7 @@ "execution_count": 36, "id": "94ff654c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.151742Z", - "iopub.status.busy": "2023-08-22T07:01:02.151594Z", - "iopub.status.idle": "2023-08-22T07:01:02.271388Z", - "shell.execute_reply": "2023-08-22T07:01:02.270932Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1883,12 +1703,7 @@ "execution_count": 37, "id": "b3561317", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.274358Z", - "iopub.status.busy": "2023-08-22T07:01:02.273928Z", - "iopub.status.idle": "2023-08-22T07:01:02.290763Z", - "shell.execute_reply": "2023-08-22T07:01:02.290332Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1956,12 +1771,7 @@ "execution_count": 38, "id": "be9e4f9c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.293285Z", - "iopub.status.busy": "2023-08-22T07:01:02.293108Z", - "iopub.status.idle": "2023-08-22T07:01:02.297337Z", - "shell.execute_reply": "2023-08-22T07:01:02.296824Z" - } + "execution": {} }, "outputs": [ { @@ -2003,12 +1813,7 @@ "execution_count": 39, "id": "f80d8563", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.299662Z", - "iopub.status.busy": "2023-08-22T07:01:02.299457Z", - "iopub.status.idle": "2023-08-22T07:01:02.303357Z", - "shell.execute_reply": "2023-08-22T07:01:02.302779Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2036,12 +1841,7 @@ "execution_count": 40, "id": "83e7ccf8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.305759Z", - "iopub.status.busy": "2023-08-22T07:01:02.305577Z", - "iopub.status.idle": "2023-08-22T07:01:02.310695Z", - "shell.execute_reply": "2023-08-22T07:01:02.310382Z" - } + "execution": {} }, "outputs": [ { @@ -2096,12 +1896,7 @@ "execution_count": 41, "id": "56ee8cbf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.312692Z", - "iopub.status.busy": "2023-08-22T07:01:02.312323Z", - "iopub.status.idle": "2023-08-22T07:01:02.538970Z", - "shell.execute_reply": "2023-08-22T07:01:02.538582Z" - } + "execution": {} }, "outputs": [ { @@ -2141,12 +1936,7 @@ "execution_count": 42, "id": "10f4fc97", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.541376Z", - "iopub.status.busy": "2023-08-22T07:01:02.541208Z", - "iopub.status.idle": "2023-08-22T07:01:02.758968Z", - "shell.execute_reply": "2023-08-22T07:01:02.758508Z" - } + "execution": {} }, "outputs": [ { @@ -2183,12 +1973,7 @@ "execution_count": 43, "id": "3aed342a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.761325Z", - "iopub.status.busy": "2023-08-22T07:01:02.761152Z", - "iopub.status.idle": "2023-08-22T07:01:02.765136Z", - "shell.execute_reply": "2023-08-22T07:01:02.764674Z" - } + "execution": {} }, "outputs": [ { @@ -2225,12 +2010,7 @@ "execution_count": 44, "id": "49c6db0c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.767317Z", - "iopub.status.busy": "2023-08-22T07:01:02.767155Z", - "iopub.status.idle": "2023-08-22T07:01:02.771483Z", - "shell.execute_reply": "2023-08-22T07:01:02.771020Z" - } + "execution": {} }, "outputs": [ { @@ -2311,12 +2091,7 @@ "execution_count": 45, "id": "0ef4b7ec", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:02.773805Z", - "iopub.status.busy": "2023-08-22T07:01:02.773647Z", - "iopub.status.idle": "2023-08-22T07:01:03.002349Z", - "shell.execute_reply": "2023-08-22T07:01:03.001396Z" - } + "execution": {} }, "outputs": [ { @@ -2366,12 +2141,7 @@ "execution_count": 46, "id": "51761ef3", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:03.007714Z", - "iopub.status.busy": "2023-08-22T07:01:03.007387Z", - "iopub.status.idle": "2023-08-22T07:01:03.189542Z", - "shell.execute_reply": "2023-08-22T07:01:03.189234Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2418,12 +2188,7 @@ "execution_count": 47, "id": "3dbe7baf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:03.191496Z", - "iopub.status.busy": "2023-08-22T07:01:03.191325Z", - "iopub.status.idle": "2023-08-22T07:01:03.200433Z", - "shell.execute_reply": "2023-08-22T07:01:03.199967Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2451,12 +2216,7 @@ "execution_count": 48, "id": "8f4e9db0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:03.206306Z", - "iopub.status.busy": "2023-08-22T07:01:03.206039Z", - "iopub.status.idle": "2023-08-22T07:01:03.211349Z", - "shell.execute_reply": "2023-08-22T07:01:03.210526Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2488,12 +2248,7 @@ "execution_count": 49, "id": "6373db4d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:03.215280Z", - "iopub.status.busy": "2023-08-22T07:01:03.215107Z", - "iopub.status.idle": "2023-08-22T07:01:03.220745Z", - "shell.execute_reply": "2023-08-22T07:01:03.220268Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -2544,12 +2299,7 @@ "execution_count": 50, "id": "9f185f83", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:03.224010Z", - "iopub.status.busy": "2023-08-22T07:01:03.223805Z", - "iopub.status.idle": "2023-08-22T07:01:03.756454Z", - "shell.execute_reply": "2023-08-22T07:01:03.755090Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2576,12 +2326,7 @@ "execution_count": 51, "id": "b044b197", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:03.760531Z", - "iopub.status.busy": "2023-08-22T07:01:03.759964Z", - "iopub.status.idle": "2023-08-22T07:01:04.106389Z", - "shell.execute_reply": "2023-08-22T07:01:04.105861Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2646,12 +2391,7 @@ "execution_count": 52, "id": "b2450bb2", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:04.108911Z", - "iopub.status.busy": "2023-08-22T07:01:04.108756Z", - "iopub.status.idle": "2023-08-22T07:01:04.295709Z", - "shell.execute_reply": "2023-08-22T07:01:04.295142Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2719,12 +2459,7 @@ "execution_count": 53, "id": "f3f85512", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:04.298506Z", - "iopub.status.busy": "2023-08-22T07:01:04.298369Z", - "iopub.status.idle": "2023-08-22T07:01:04.301034Z", - "shell.execute_reply": "2023-08-22T07:01:04.300583Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -2757,12 +2492,7 @@ "execution_count": 54, "id": "5cbeeb19", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:04.303691Z", - "iopub.status.busy": "2023-08-22T07:01:04.303453Z", - "iopub.status.idle": "2023-08-22T07:01:05.507632Z", - "shell.execute_reply": "2023-08-22T07:01:05.507177Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -2810,12 +2540,7 @@ "execution_count": 55, "id": "1eb3c92e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:05.509745Z", - "iopub.status.busy": "2023-08-22T07:01:05.509610Z", - "iopub.status.idle": "2023-08-22T07:01:05.520162Z", - "shell.execute_reply": "2023-08-22T07:01:05.519790Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -3004,12 +2729,7 @@ "execution_count": 56, "id": "e3c2841c", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:05.522420Z", - "iopub.status.busy": "2023-08-22T07:01:05.522186Z", - "iopub.status.idle": "2023-08-22T07:01:05.914218Z", - "shell.execute_reply": "2023-08-22T07:01:05.913893Z" - } + "execution": {} }, "outputs": [ { @@ -3053,12 +2773,7 @@ "execution_count": 57, "id": "94dfe5a0", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:05.915828Z", - "iopub.status.busy": "2023-08-22T07:01:05.915706Z", - "iopub.status.idle": "2023-08-22T07:01:06.390699Z", - "shell.execute_reply": "2023-08-22T07:01:06.390367Z" - } + "execution": {} }, "outputs": [ { @@ -3178,12 +2893,7 @@ "execution_count": 58, "id": "abd51940", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:06.404283Z", - "iopub.status.busy": "2023-08-22T07:01:06.404155Z", - "iopub.status.idle": "2023-08-22T07:01:06.897582Z", - "shell.execute_reply": "2023-08-22T07:01:06.897221Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ diff --git a/Ch13-multiple-lab.ipynb b/Ch13-multiple-lab.ipynb index 0e8886d..9ed586b 100644 --- a/Ch13-multiple-lab.ipynb +++ b/Ch13-multiple-lab.ipynb @@ -25,12 +25,7 @@ "execution_count": 1, "id": "7cc4fbeb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:10.471948Z", - "iopub.status.busy": "2023-08-22T07:01:10.471668Z", - "iopub.status.idle": "2023-08-22T07:01:11.350943Z", - "shell.execute_reply": "2023-08-22T07:01:11.350643Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -55,12 +50,7 @@ "execution_count": 2, "id": "595efc18", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.352807Z", - "iopub.status.busy": "2023-08-22T07:01:11.352663Z", - "iopub.status.idle": "2023-08-22T07:01:11.354553Z", - "shell.execute_reply": "2023-08-22T07:01:11.354339Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [], @@ -94,12 +84,7 @@ "execution_count": 3, "id": "985d1d6e", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.355962Z", - "iopub.status.busy": "2023-08-22T07:01:11.355865Z", - "iopub.status.idle": "2023-08-22T07:01:11.357829Z", - "shell.execute_reply": "2023-08-22T07:01:11.357602Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -124,12 +109,7 @@ "execution_count": 4, "id": "753d612a", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.359164Z", - "iopub.status.busy": "2023-08-22T07:01:11.359072Z", - "iopub.status.idle": "2023-08-22T07:01:11.362315Z", - "shell.execute_reply": "2023-08-22T07:01:11.362065Z" - } + "execution": {} }, "outputs": [ { @@ -171,12 +151,7 @@ "execution_count": 5, "id": "facd6569", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.363675Z", - "iopub.status.busy": "2023-08-22T07:01:11.363598Z", - "iopub.status.idle": "2023-08-22T07:01:11.382551Z", - "shell.execute_reply": "2023-08-22T07:01:11.382313Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [], @@ -207,12 +182,7 @@ "execution_count": 6, "id": "e89ef3eb", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.384012Z", - "iopub.status.busy": "2023-08-22T07:01:11.383929Z", - "iopub.status.idle": "2023-08-22T07:01:11.390854Z", - "shell.execute_reply": "2023-08-22T07:01:11.390622Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -308,12 +278,7 @@ "execution_count": 7, "id": "ae184aaf", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.392278Z", - "iopub.status.busy": "2023-08-22T07:01:11.392193Z", - "iopub.status.idle": "2023-08-22T07:01:11.414541Z", - "shell.execute_reply": "2023-08-22T07:01:11.414284Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -420,12 +385,7 @@ "execution_count": 8, "id": "0295fe68", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.416013Z", - "iopub.status.busy": "2023-08-22T07:01:11.415934Z", - "iopub.status.idle": "2023-08-22T07:01:11.601337Z", - "shell.execute_reply": "2023-08-22T07:01:11.601050Z" - } + "execution": {} }, "outputs": [ { @@ -476,12 +436,7 @@ "execution_count": 9, "id": "406e59a8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.603043Z", - "iopub.status.busy": "2023-08-22T07:01:11.602922Z", - "iopub.status.idle": "2023-08-22T07:01:11.637677Z", - "shell.execute_reply": "2023-08-22T07:01:11.637393Z" - } + "execution": {} }, "outputs": [ { @@ -544,12 +499,7 @@ "execution_count": 10, "id": "d4f6a247", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.639421Z", - "iopub.status.busy": "2023-08-22T07:01:11.639308Z", - "iopub.status.idle": "2023-08-22T07:01:11.641724Z", - "shell.execute_reply": "2023-08-22T07:01:11.641464Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -583,12 +533,7 @@ "execution_count": 11, "id": "01a29d71", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.643173Z", - "iopub.status.busy": "2023-08-22T07:01:11.643074Z", - "iopub.status.idle": "2023-08-22T07:01:11.645284Z", - "shell.execute_reply": "2023-08-22T07:01:11.645035Z" - } + "execution": {} }, "outputs": [ { @@ -625,12 +570,7 @@ "execution_count": 12, "id": "95454eb4", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.646828Z", - "iopub.status.busy": "2023-08-22T07:01:11.646723Z", - "iopub.status.idle": "2023-08-22T07:01:11.681328Z", - "shell.execute_reply": "2023-08-22T07:01:11.681006Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -665,12 +605,7 @@ "execution_count": 13, "id": "1f1ac764", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.683039Z", - "iopub.status.busy": "2023-08-22T07:01:11.682931Z", - "iopub.status.idle": "2023-08-22T07:01:11.685981Z", - "shell.execute_reply": "2023-08-22T07:01:11.685726Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -709,12 +644,7 @@ "execution_count": 14, "id": "298d975d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.687516Z", - "iopub.status.busy": "2023-08-22T07:01:11.687436Z", - "iopub.status.idle": "2023-08-22T07:01:11.690137Z", - "shell.execute_reply": "2023-08-22T07:01:11.689879Z" - } + "execution": {} }, "outputs": [ { @@ -761,12 +691,7 @@ "execution_count": 15, "id": "be117713", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:11.691729Z", - "iopub.status.busy": "2023-08-22T07:01:11.691643Z", - "iopub.status.idle": "2023-08-22T07:01:12.083351Z", - "shell.execute_reply": "2023-08-22T07:01:12.083006Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -821,12 +746,7 @@ "execution_count": 16, "id": "537c4ea8", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.085028Z", - "iopub.status.busy": "2023-08-22T07:01:12.084925Z", - "iopub.status.idle": "2023-08-22T07:01:12.158318Z", - "shell.execute_reply": "2023-08-22T07:01:12.158031Z" - } + "execution": {} }, "outputs": [ { @@ -862,12 +782,7 @@ "execution_count": 17, "id": "2c88ec87", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.160012Z", - "iopub.status.busy": "2023-08-22T07:01:12.159900Z", - "iopub.status.idle": "2023-08-22T07:01:12.510866Z", - "shell.execute_reply": "2023-08-22T07:01:12.510573Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -891,12 +806,7 @@ "execution_count": 18, "id": "b6d56819", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.512518Z", - "iopub.status.busy": "2023-08-22T07:01:12.512432Z", - "iopub.status.idle": "2023-08-22T07:01:12.514982Z", - "shell.execute_reply": "2023-08-22T07:01:12.514769Z" - } + "execution": {} }, "outputs": [ { @@ -936,12 +846,7 @@ "execution_count": 19, "id": "b00da3a1", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.516361Z", - "iopub.status.busy": "2023-08-22T07:01:12.516283Z", - "iopub.status.idle": "2023-08-22T07:01:12.518438Z", - "shell.execute_reply": "2023-08-22T07:01:12.518191Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -980,12 +885,7 @@ "execution_count": 20, "id": "1c230117", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.519863Z", - "iopub.status.busy": "2023-08-22T07:01:12.519780Z", - "iopub.status.idle": "2023-08-22T07:01:12.521828Z", - "shell.execute_reply": "2023-08-22T07:01:12.521610Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1030,12 +930,7 @@ "execution_count": 21, "id": "62289650", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.523116Z", - "iopub.status.busy": "2023-08-22T07:01:12.523043Z", - "iopub.status.idle": "2023-08-22T07:01:12.525236Z", - "shell.execute_reply": "2023-08-22T07:01:12.525021Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1064,12 +959,7 @@ "execution_count": 22, "id": "18b3c0ed", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.526613Z", - "iopub.status.busy": "2023-08-22T07:01:12.526539Z", - "iopub.status.idle": "2023-08-22T07:01:12.740275Z", - "shell.execute_reply": "2023-08-22T07:01:12.739986Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1114,12 +1004,7 @@ "execution_count": 23, "id": "eb79e606", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.742002Z", - "iopub.status.busy": "2023-08-22T07:01:12.741882Z", - "iopub.status.idle": "2023-08-22T07:01:12.844984Z", - "shell.execute_reply": "2023-08-22T07:01:12.844724Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1165,12 +1050,7 @@ "execution_count": 24, "id": "1afbcf47", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.846468Z", - "iopub.status.busy": "2023-08-22T07:01:12.846384Z", - "iopub.status.idle": "2023-08-22T07:01:12.849714Z", - "shell.execute_reply": "2023-08-22T07:01:12.849454Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1218,12 +1098,7 @@ "execution_count": 25, "id": "f73f4c6d", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:12.851134Z", - "iopub.status.busy": "2023-08-22T07:01:12.851053Z", - "iopub.status.idle": "2023-08-22T07:01:14.848052Z", - "shell.execute_reply": "2023-08-22T07:01:14.847788Z" - }, + "execution": {}, "lines_to_next_cell": 2 }, "outputs": [ @@ -1269,12 +1144,7 @@ "execution_count": 26, "id": "062daf19", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:14.849560Z", - "iopub.status.busy": "2023-08-22T07:01:14.849477Z", - "iopub.status.idle": "2023-08-22T07:01:14.990286Z", - "shell.execute_reply": "2023-08-22T07:01:14.989980Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ @@ -1329,12 +1199,7 @@ "execution_count": 27, "id": "6d14fcad", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:01:14.992440Z", - "iopub.status.busy": "2023-08-22T07:01:14.992326Z", - "iopub.status.idle": "2023-08-22T07:04:36.802040Z", - "shell.execute_reply": "2023-08-22T07:04:36.801740Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1375,12 +1240,7 @@ "execution_count": 28, "id": "8f0ec909", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:04:36.803805Z", - "iopub.status.busy": "2023-08-22T07:04:36.803712Z", - "iopub.status.idle": "2023-08-22T07:04:36.913862Z", - "shell.execute_reply": "2023-08-22T07:04:36.913579Z" - } + "execution": {} }, "outputs": [], "source": [ @@ -1416,12 +1276,7 @@ "execution_count": 29, "id": "f11339e5", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:04:36.915631Z", - "iopub.status.busy": "2023-08-22T07:04:36.915542Z", - "iopub.status.idle": "2023-08-22T07:04:36.918059Z", - "shell.execute_reply": "2023-08-22T07:04:36.917797Z" - } + "execution": {} }, "outputs": [ { @@ -1471,12 +1326,7 @@ "execution_count": 30, "id": "d2600773", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:04:36.919428Z", - "iopub.status.busy": "2023-08-22T07:04:36.919345Z", - "iopub.status.idle": "2023-08-22T07:04:36.921649Z", - "shell.execute_reply": "2023-08-22T07:04:36.921430Z" - } + "execution": {} }, "outputs": [ { @@ -1539,12 +1389,7 @@ "execution_count": 31, "id": "924b7705", "metadata": { - "execution": { - "iopub.execute_input": "2023-08-22T07:04:36.923042Z", - "iopub.status.busy": "2023-08-22T07:04:36.922969Z", - "iopub.status.idle": "2023-08-22T07:04:36.986633Z", - "shell.execute_reply": "2023-08-22T07:04:36.986366Z" - }, + "execution": {}, "lines_to_next_cell": 0 }, "outputs": [ From 58c0fa0b098e908cfe6dfa3971c5a7a5fd07792e Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:20:09 -0700 Subject: [PATCH 16/18] stripped output --- Ch02-statlearn-lab.ipynb | 35 ++++++++++++++++++++--------------- 1 file changed, 20 insertions(+), 15 deletions(-) diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb index e3cf777..1ad4eec 100644 --- a/Ch02-statlearn-lab.ipynb +++ b/Ch02-statlearn-lab.ipynb @@ -1107,16 +1107,16 @@ { "data": { "text/plain": [ - "array([-0.83511556, 0.43461482, -1.3810022 , -0.64162363, -0.86270682,\n", - " 0.6660104 , 0.17543353, 0.68220139, -0.20392851, -1.47534629,\n", - " 0.18438201, 1.50157883, 0.47473968, -1.33062844, 1.57614184,\n", - " -0.25596784, 0.66848396, -1.5410464 , 0.25298297, -0.79118181,\n", - " -0.49399731, -1.45139182, 0.71261617, 1.19862796, 0.52772362,\n", - " 0.57343239, -0.01577204, 2.56032683, -0.18936979, -0.09651695,\n", - " -1.74331796, 0.11390389, 0.27595492, 1.2606086 , 0.49282705,\n", - " 0.60603827, 0.24699795, 0.28636264, 0.54683924, 0.45133321,\n", - " 0.92563958, 1.20847512, 0.40993092, 0.01951345, 0.27271415,\n", - " 0.94231124, 0.47471813, 2.12851232, 0.128139 , -0.56681371])" + "array([ 0.37697843, -0.1714562 , -0.07387699, 0.88289542, 0.42403407,\n", + " -1.25834631, -0.56281024, 0.96180301, -0.81464709, -0.18611985,\n", + " 1.55473926, 0.90549152, -1.36748952, -0.04172983, 1.33669081,\n", + " 0.4353957 , 1.14591958, 0.0858525 , 0.72945623, -0.57270057,\n", + " 1.30960282, 0.10564808, 0.32318427, 0.73300473, -1.16608308,\n", + " -1.76114586, 0.69877186, -0.30159982, -1.16892964, 0.49920879,\n", + " -0.04008576, 0.99724065, 0.41002051, 0.83231347, -0.42308487,\n", + " -2.17126985, 1.84862662, 0.40182298, -0.03440717, 0.05143581,\n", + " 2.01972242, -0.41715298, -1.02696106, 0.80886424, 0.93826288,\n", + " -1.08741113, -1.0994228 , 0.39633369, 0.29890158, -0.51821285])" ] }, "execution_count": 28, @@ -1170,8 +1170,8 @@ { "data": { "text/plain": [ - "array([[1. , 0.65985439],\n", - " [0.65985439, 1. ]])" + "array([[1. , 0.65421904],\n", + " [0.65421904, 1. ]])" ] }, "execution_count": 30, @@ -1207,8 +1207,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 6.33922704 -0.66068102]\n", - "[-6.96104352 18.61644142]\n" + "[-5.49291495 2.33352471]\n", + "[8.45050027 0.00908238]\n" ] } ], @@ -1677,7 +1677,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -7711,6 +7711,11 @@ "formats": "Rmd,ipynb", "main_language": "python" }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", From 8a59c868d28d5c252d3417b3cf108c88fad5b6c1 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:21:22 -0700 Subject: [PATCH 17/18] Ch02 Rmd --- Ch02-statlearn-lab.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/Ch02-statlearn-lab.Rmd b/Ch02-statlearn-lab.Rmd index 67c88ef..65fb279 100644 --- a/Ch02-statlearn-lab.Rmd +++ b/Ch02-statlearn-lab.Rmd @@ -3,7 +3,6 @@ jupyter: jupytext: cell_metadata_filter: -all formats: Rmd,ipynb - main_language: python text_representation: extension: .Rmd format_name: rmarkdown From f4ecee2e614dcd34c13e573568b02dcfc5da99b4 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Tue, 22 Aug 2023 00:25:12 -0700 Subject: [PATCH 18/18] updating README --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e3d1f97..515e560 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ISLP_labs -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.1) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.1.2) Up-to-date version of labs for ISLP. @@ -12,7 +12,7 @@ intent is that building a conda environment with To install the current version of the requirements run ``` -pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2.1/requirements.txt; +pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2.1.2/requirements.txt; ``` The labs can now be run from this directory: