updating version

2026-02-02 15:51:46 -08:00
parent 681c8d2b4d
commit 39a00cc02d
26 changed files with 1528 additions and 2240 deletions
--- a/Ch02-statlearn-lab.Rmd
+++ b/Ch02-statlearn-lab.Rmd
@@ -2,22 +2,22 @@
 jupyter:
  jupytext:
    cell_metadata_filter: -all
-    formats: ipynb,Rmd
+    formats: Rmd
    main_language: python
    text_representation:
      extension: .Rmd
      format_name: rmarkdown
      format_version: '1.2'
-      jupytext_version: 1.16.7
+      jupytext_version: 1.19.1
 ---

 # Introduction to Python

-<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch02-statlearn-lab.ipynb">
+<a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2.1/Ch02-statlearn-lab.ipynb">
 <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>

-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch02-statlearn-lab.ipynb)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2.1?labpath=Ch02-statlearn-lab.ipynb)



@@ -83,7 +83,7 @@ print('fit a model with', 11, 'variables')
 The following command will provide information about the `print()` function.

 ```{python}
-# print?
+print?

 ```

@@ -233,7 +233,7 @@ documentation associated with the function `fun`, if it exists.
 We can try this for `np.array()`. 

 ```{python}
-# np.array?
+np.array?

 ```
 This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`.
@@ -904,7 +904,6 @@ A[np.array([0,1,0,1])]

 By contrast, `keep_rows` retrieves only the second and fourth rows  of `A` --- i.e. the rows for which the Boolean equals `True`. 

-
 ```{python}
 A[keep_rows]

@@ -978,9 +977,10 @@ Auto
 The book website also has a whitespace-delimited version of this data, called `Auto.data`. This can be read in as follows:

 ```{python}
-Auto = pd.read_csv('Auto.data', delim_whitespace=True)
+Auto = pd.read_csv("Auto.data", sep="\\s+")

 ```
+
 Both `Auto.csv` and `Auto.data` are simply text
 files. Before loading data into `Python`, it is a good idea to view it using
 a text editor or other software, such as Microsoft Excel.
@@ -1015,12 +1015,11 @@ value `np.nan`, which means *not a number*:
 ```{python}
 Auto = pd.read_csv('Auto.data',
                   na_values=['?'],
-                   delim_whitespace=True)
+                   sep="\\s+")
 Auto['horsepower'].sum()

 ```

-
 The `Auto.shape`  attribute tells us that the data has 397
 observations, or rows, and nine variables, or columns.

@@ -1293,14 +1292,13 @@ because `Python` does not know to look in the  `Auto`  data set for those variab

 ```{python}
 fig, ax = subplots(figsize=(8, 8))
-ax.plot(horsepower, mpg, 'o');
+ax.plot(horsepower, mpg, 'o')
 ```
 We can address this by accessing the columns directly:

 ```{python}
 fig, ax = subplots(figsize=(8, 8))
 ax.plot(Auto['horsepower'], Auto['mpg'], 'o');
-
 ```
 Alternatively, we can use the `plot()` method with the call `Auto.plot()`.
 Using this method,
@@ -1402,8 +1400,7 @@ Auto['cylinders'].describe()
 Auto['mpg'].describe()

 ```
-To exit `Jupyter`,  select `File / Shut Down`.
-
- 

+```{python}

+```
--- a/Ch02-statlearn-lab.ipynb
+++ b/Ch02-statlearn-lab.ipynb
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch03-linreg-lab.ipynb
+++ b/Ch03-linreg-lab.ipynb
@@ -1507,8 +1507,8 @@
   "source": [
    "ax = Boston.plot.scatter('lstat', 'medv')\n",
    "abline(ax,\n",
-    "       results.params[0],\n",
-    "       results.params[1],\n",
+    "       results.params['intercept'],\n",
+    "       results.params['lstat'],\n",
    "       'r--',\n",
    "       linewidth=3)\n"
   ]
@@ -2970,9 +2970,14 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
@@ -2983,7 +2988,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.4"
  }
 },
 "nbformat": 4,
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch04-classification-lab.ipynb
+++ b/Ch04-classification-lab.ipynb
@@ -6241,7 +6241,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
  "language_info": {
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch05-resample-lab.ipynb
+++ b/Ch05-resample-lab.ipynb
@@ -1283,9 +1283,14 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
@@ -1296,7 +1301,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.4"
  }
 },
 "nbformat": 4,
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
@@ -148,6 +148,7 @@ hitters_MSE = sklearn_selected(OLS,
                               strategy)
 hitters_MSE.fit(Hitters, Y)
 hitters_MSE.selected_state_
+
 ```

 Using `neg_Cp` results in a smaller model, as expected, with just 10 variables selected.
--- a/Ch06-varselect-lab.ipynb
+++ b/Ch06-varselect-lab.ipynb
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch07-nonlin-lab.ipynb
+++ b/Ch07-nonlin-lab.ipynb
@@ -3296,7 +3296,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
  "language_info": {
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch08-baggboost-lab.ipynb
+++ b/Ch08-baggboost-lab.ipynb
@@ -3378,7 +3378,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
  "language_info": {
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch09-svm-lab.ipynb
+++ b/Ch09-svm-lab.ipynb
@@ -2701,7 +2701,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
  "language_info": {
--- a/Ch10-deeplearning-lab.Rmd
+++ b/Ch10-deeplearning-lab.Rmd
@@ -2,13 +2,13 @@
 jupyter:
  jupytext:
    cell_metadata_filter: -all
-    formats: ipynb,Rmd
+    formats: Rmd
    main_language: python
    text_representation:
      extension: .Rmd
      format_name: rmarkdown
      format_version: '1.2'
-      jupytext_version: 1.15.0
+      jupytext_version: 1.19.1
 ---

 # Deep Learning
@@ -1861,7 +1861,3 @@ nl_trainer.test(nl_module, datamodule=day_dm)

 

-
-```{python}
-
-```
--- a/Ch10-deeplearning-lab.ipynb
+++ b/Ch10-deeplearning-lab.ipynb
@@ -4565,7 +4565,7 @@
    "datasets = []\n",
    "for mask in [train, ~train]:\n",
    "    X_rnn_t = torch.tensor(X_rnn[mask].astype(np.float32))\n",
-    "    Y_t = torch.tensor(Y[mask].astype(np.float32))\n",
+    "    Y_t = torch.tensor(np.asarray(Y[mask], np.float32))\n",
    "    datasets.append(TensorDataset(X_rnn_t, Y_t))\n",
    "nyse_train, nyse_test = datasets\n"
   ]
@@ -4955,8 +4955,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
-   "main_language": "python"
+   "formats": "ipynb"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
@@ -4973,7 +4972,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.11.4"
  }
 },
 "nbformat": 4,
--- a/Ch11-surv-lab.Rmd
+++ b/Ch11-surv-lab.Rmd
@@ -2,7 +2,7 @@
 jupyter:
  jupytext:
    cell_metadata_filter: -all
-    formats: ipynb,Rmd
+    formats: Rmd
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch11-surv-lab.ipynb
+++ b/Ch11-surv-lab.ipynb
@@ -2733,7 +2733,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
  "language_info": {
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch12-unsup-lab.ipynb
+++ b/Ch12-unsup-lab.ipynb
@@ -4622,7 +4622,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
  "language_info": {
--- 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
    main_language: python
    text_representation:
      extension: .Rmd
--- a/Ch13-multiple-lab.ipynb
+++ b/Ch13-multiple-lab.ipynb
@@ -1584,7 +1584,7 @@
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
-   "formats": "ipynb,Rmd",
+   "formats": "ipynb",
   "main_language": "python"
  },
  "language_info": {
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ intent is that building a virtual environment with
 To install the current version of the requirements run

 ```
-pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2.2/requirements.txt;
+pip install -r https://raw.githubusercontent.com/intro-stat-learning/ISLP_labs/v2.2.1/requirements.txt;
 ```

 The labs can now be run via:
@@ -46,7 +46,7 @@ jupyter lab Ch02-statlearn-lab.ipynb

 # Zip / tarball

-You can download all the labs as a `.zip` or `.tar.gz` [here](https://github.com/intro-stat-learning/ISLP_labs/releases/tag/v2.2)
+You can download all the labs as a `.zip` or `.tar.gz` [here](https://github.com/intro-stat-learning/ISLP_labs/releases/tag/v2.2.1)


 ## Contributors ✨
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,16 +1,16 @@
-numpy==1.26.4
-scipy==1.11.4
-pandas==2.2.2
-lxml==5.2.2
-scikit-learn==1.5.0
-joblib==1.4.2
-statsmodels==0.14.2
-lifelines==0.28.0
-pygam==0.9.1
-l0bnb==1.0.0
-torch==2.3.0
-torchvision==0.18.0
-pytorch-lightning==2.2.5
-torchinfo==1.8.0
-torchmetrics==1.4.0.post0
-ISLP==0.4.0
+numpy==2.4.2
+scipy==1.16.3
+pandas==3.0.0
+lxml==6.0.2
+scikit-learn==1.8.0
+joblib==1.5.3
+statsmodels==0.14.6
+lifelines==0.30.0
+pygam==0.12.0
+git+https://github.com/jonathan-taylor/l0bnb.git@fix_inf#egg=l0bnb
+torch==2.10.0
+torchvision==0.25.0
+pytorch-lightning==2.6.1
+torchinfo==1.8.0
+torchmetrics==1.8.2
+ISLP==0.4.1