diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb
index 516f3c8..18f79f3 100644
--- a/Ch10-deeplearning-lab.ipynb
+++ b/Ch10-deeplearning-lab.ipynb
@@ -5,7 +5,6 @@
    "id": "fd22c2e6",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 10\n",
     "\n",
     "# Lab: Deep Learning\n",
@@ -25,9 +24,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "aeb913e2",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np, pandas as pd\n",
@@ -68,7 +65,7 @@
     "import torch\n",
     "from torch import nn\n",
     "from torch.optim import RMSprop\n",
-    "from torch.utils.data import TensorDataset\n"
+    "from torch.utils.data import TensorDataset"
    ]
   },
   {
@@ -94,7 +91,7 @@
     "from torchmetrics import (MeanAbsoluteError,\n",
     "                          R2Score)\n",
     "from torchinfo import summary\n",
-    "from torchvision.io import read_image\n"
+    "from torchvision.io import read_image"
    ]
   },
   {
@@ -117,7 +114,7 @@
    "outputs": [],
    "source": [
     "from pytorch_lightning import Trainer\n",
-    "from pytorch_lightning.loggers import CSVLogger\n"
+    "from pytorch_lightning.loggers import CSVLogger"
    ]
   },
   {
@@ -144,9 +141,9 @@
     }
    ],
    "source": [
-    "from pytorch_lightning.utilities.seed import seed_everything\n",
+    "from pytorch_lightning import seed_everything\n",
     "seed_everything(0, workers=True)\n",
-    "torch.use_deterministic_algorithms(True, warn_only=True)\n"
+    "torch.use_deterministic_algorithms(True, warn_only=True)"
    ]
   },
   {
@@ -163,9 +160,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "c85308d1",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from torchvision.datasets import MNIST, CIFAR100\n",
@@ -204,7 +199,7 @@
     "from ISLP.torch import (SimpleDataModule,\n",
     "                        SimpleModule,\n",
     "                        ErrorTracker,\n",
-    "                        rec_num_workers)\n"
+    "                        rec_num_workers)"
    ]
   },
   {
@@ -233,7 +228,7 @@
     "from ISLP.torch.imdb import (load_lookup,\n",
     "                             load_tensor,\n",
     "                             load_sparse,\n",
-    "                             load_sequential)\n"
+    "                             load_sequential)"
    ]
   },
   {
@@ -256,13 +251,11 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "493821a9",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from glob import glob\n",
-    "import json\n"
+    "import json"
    ]
   },
   {
@@ -278,13 +271,11 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "a427e224",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "Hitters = load_data('Hitters').dropna()\n",
-    "n = Hitters.shape[0]\n"
+    "n = Hitters.shape[0]"
    ]
   },
   {
@@ -306,14 +297,12 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "feac4b15",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = MS(Hitters.columns.drop('Salary'), intercept=False)\n",
     "X = model.fit_transform(Hitters).to_numpy()\n",
-    "Y = Hitters['Salary'].to_numpy()\n"
+    "Y = Hitters['Salary'].to_numpy()"
    ]
   },
   {
@@ -396,7 +385,7 @@
     "The specialized solver we used in Section 6.5.2 uses only mean squared error. So here, with a bit more work,  we create a cross-validation grid and perform the cross-validation directly.  \n",
     "\n",
     "We encode a pipeline with two steps: we first normalize the features using a `StandardScaler()` transform,\n",
-    "and then fit the lasso without further normalization. "
+    "and then fit the lasso without further normalization."
    ]
   },
   {
@@ -426,9 +415,7 @@
    "cell_type": "code",
    "execution_count": 15,
    "id": "2b55b38b",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_s = scaler.fit_transform(X_train)\n",
@@ -478,9 +465,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "1bd1fd13",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -513,7 +498,7 @@
     "Typically this is done in  `pytorch` by sub-classing a generic\n",
     "representation of a network, which is the approach we take here.\n",
     "Although this example is simple, we will go through the steps in some detail, since it will serve us well\n",
-    "for the more complex examples to follow.\n"
+    "for the more complex examples to follow."
    ]
   },
   {
@@ -536,7 +521,7 @@
     "\n",
     "    def forward(self, x):\n",
     "        x = self.flatten(x)\n",
-    "        return torch.flatten(self.sequential(x))\n"
+    "        return torch.flatten(self.sequential(x))"
    ]
   },
   {
@@ -581,7 +566,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hit_model = HittersModel(X.shape[1])\n"
+    "hit_model = HittersModel(X.shape[1])"
    ]
   },
   {
@@ -594,9 +579,7 @@
     "for the weights and *intercept*  of the map (often called the *bias*). This layer\n",
     "is then mapped to a ReLU layer followed by a 40% dropout layer, and finally a\n",
     "linear map down to 1 dimension, again with a bias. The total number of\n",
-    "trainable parameters is therefore $50\\times 19+50+50+1=1051$.\n",
-    "\n",
-    "    "
+    "trainable parameters is therefore $50\\times 19+50+50+1=1051$."
    ]
   },
   {
@@ -606,27 +589,15 @@
    "source": [
     "The package `torchinfo` provides a `summary()` function that neatly summarizes\n",
     "this information. We specify the size of the input and see the size\n",
-    "of each tensor as it passes through layers of the network. "
+    "of each tensor as it passes through layers of the network."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "id": "2f20059e",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return super().__sizeof__() + self.nbytes()\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -644,7 +615,7 @@
        "Total params: 1,051\n",
        "Trainable params: 1,051\n",
        "Non-trainable params: 0\n",
-       "Total mult-adds (M): 0.18\n",
+       "Total mult-adds (Units.MEGABYTES): 0.18\n",
        "===================================================================================================================\n",
        "Input size (MB): 0.01\n",
        "Forward/backward pass size (MB): 0.07\n",
@@ -663,7 +634,7 @@
     "        input_size=X_train.shape,\n",
     "        col_names=['input_size',\n",
     "                   'output_size',\n",
-    "                   'num_params'])\n"
+    "                   'num_params'])"
    ]
   },
   {
@@ -691,9 +662,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "6ca3030d",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_train_t = torch.tensor(X_train.astype(np.float32))\n",
@@ -718,7 +687,7 @@
    "source": [
     "X_test_t = torch.tensor(X_test.astype(np.float32))\n",
     "Y_test_t = torch.tensor(Y_test.astype(np.float32))\n",
-    "hit_test = TensorDataset(X_test_t, Y_test_t)\n"
+    "hit_test = TensorDataset(X_test_t, Y_test_t)"
    ]
   },
   {
@@ -786,8 +755,8 @@
     "hit_dm = SimpleDataModule(hit_train,\n",
     "                          hit_test,\n",
     "                          batch_size=32,\n",
-    "                          num_workers=min(4, max_num_workers),\n",
-    "                          validation=hit_test)\n"
+    "                          num_workers=4, #min(4, max_num_workers),\n",
+    "                          validation=hit_test)"
    ]
   },
   {
@@ -812,7 +781,7 @@
    "outputs": [],
    "source": [
     "hit_module = SimpleModule.regression(hit_model,\n",
-    "                           metrics={'mae':MeanAbsoluteError()})\n"
+    "                           metrics={'mae':MeanAbsoluteError()})"
    ]
   },
   {
@@ -867,19 +836,17 @@
    "execution_count": 27,
    "id": "1a474999",
    "metadata": {
-    "lines_to_next_cell": 0
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "GPU available: True (mps), used: False\n",
+      "GPU available: True (mps), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
       "IPU available: False, using: 0 IPUs\n",
       "HPU available: False, using: 0 HPUs\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
-      "  rank_zero_warn(\n",
       "\n",
       "  | Name  | Type         | Params\n",
       "---------------------------------------\n",
@@ -909,7 +876,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f1f820ee6fa4d81a3736b68611f3bac",
+       "model_id": "6c2c00e38c8b43738decbcb6cdf52412",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1550,20 +1517,81 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "IOPub message rate exceeded.\n",
-      "The Jupyter server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--ServerApp.iopub_msg_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
-      "ServerApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
+      "`Trainer.fit` stopped: `max_epochs=50` reached.\n"
      ]
     }
    ],
@@ -1595,14 +1623,12 @@
    "cell_type": "code",
    "execution_count": 28,
    "id": "e3b24643",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce9e57deabef4f55aba22efb507872f1",
+       "model_id": "1582c1cd9afb492193ff7bcdfc2fdd9a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1618,17 +1644,17 @@
      "output_type": "stream",
      "text": [
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
+      "Runningstage.testing metric      DataLoader 0\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "        test_loss             103304.8515625\n",
-      "        test_mae            224.26962280273438\n",
+      "        test_loss             125020.2421875\n",
+      "        test_mae             250.8108367919922\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[{'test_loss': 103304.8515625, 'test_mae': 224.26962280273438}]"
+       "[{'test_loss': 125020.2421875, 'test_mae': 250.8108367919922}]"
       ]
      },
      "execution_count": 28,
@@ -1637,7 +1663,7 @@
     }
    ],
    "source": [
-    "hit_trainer.test(hit_module, datamodule=hit_dm)\n"
+    "hit_trainer.test(hit_module, datamodule=hit_dm)"
    ]
   },
   {
@@ -1678,9 +1704,7 @@
    "cell_type": "code",
    "execution_count": 30,
    "id": "c5752a0b",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def summary_plot(results,\n",
@@ -1721,13 +1745,11 @@
    "cell_type": "code",
    "execution_count": 31,
    "id": "ff0c9fa0",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -1768,14 +1790,12 @@
    "cell_type": "code",
    "execution_count": 32,
    "id": "1ee2b61b",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(224.2696, grad_fn=<MeanBackward0>)"
+       "tensor(250.8108, grad_fn=<MeanBackward0>)"
       ]
      },
      "execution_count": 32,
@@ -1789,14 +1809,6 @@
     "torch.abs(Y_test_t - preds).mean()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8140eb0d",
-   "metadata": {},
-   "source": [
-    "    "
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "64fa7609",
@@ -1813,9 +1825,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "id": "0ca069a3",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "del(Hitters,\n",
@@ -1826,7 +1836,7 @@
     "    X_test, X_train,\n",
     "    Y_test, Y_train,\n",
     "    X_test_t, Y_test_t,\n",
-    "    hit_trainer, hit_module)\n"
+    "    hit_trainer, hit_module)"
    ]
   },
   {
@@ -1871,7 +1881,7 @@
     "                      download=True,\n",
     "                      transform=ToTensor())\n",
     "                for train in [True, False]]\n",
-    "mnist_train\n"
+    "mnist_train"
    ]
   },
   {
@@ -1908,7 +1918,7 @@
     "                            mnist_test,\n",
     "                            validation=0.2,\n",
     "                            num_workers=max_num_workers,\n",
-    "                            batch_size=256)\n"
+    "                            batch_size=256)"
    ]
   },
   {
@@ -1924,9 +1934,7 @@
    "cell_type": "code",
    "execution_count": 36,
    "id": "0c7dd6ee",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1944,7 +1952,7 @@
     "    print('X: ', X_.shape)\n",
     "    print('Y: ', Y_.shape)\n",
     "    if idx >= 1:\n",
-    "        break\n"
+    "        break"
    ]
   },
   {
@@ -2006,7 +2014,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mnist_model = MNISTModel()\n"
+    "mnist_model = MNISTModel()"
    ]
   },
   {
@@ -2055,16 +2063,6 @@
    "id": "9f502df8",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return super().__sizeof__() + self.nbytes()\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2087,7 +2085,7 @@
        "Total params: 235,146\n",
        "Trainable params: 235,146\n",
        "Non-trainable params: 0\n",
-       "Total mult-adds (M): 60.20\n",
+       "Total mult-adds (Units.MEGABYTES): 60.20\n",
        "===================================================================================================================\n",
        "Input size (MB): 0.80\n",
        "Forward/backward pass size (MB): 0.81\n",
@@ -2116,8 +2114,8 @@
    "source": [
     "Having set up both  the model and the data module, fitting this model is\n",
     "now almost identical to the `Hitters` example. In contrast to our regression model, here we will use the\n",
-    "`SimpleModule.classification()` method which\n",
-    "uses the  cross-entropy loss function instead of mean squared error."
+    "`SimpleModule.multiclass()` method which\n",
+    "uses the  cross-entropy loss function instead of mean squared error. It must be supplied with the number of classes in the problem."
    ]
   },
   {
@@ -2127,8 +2125,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mnist_module = SimpleModule.classification(mnist_model)\n",
-    "mnist_logger = CSVLogger('logs', name='MNIST')\n"
+    "mnist_module = SimpleModule.multiclass(mnist_model,\n",
+    "                                       num_classes=10)\n",
+    "mnist_logger = CSVLogger('logs', name='MNIST')"
    ]
   },
   {
@@ -2141,22 +2140,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "e2fe6de3",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "GPU available: True (mps), used: False\n",
+      "GPU available: True (mps), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
       "IPU available: False, using: 0 IPUs\n",
       "HPU available: False, using: 0 HPUs\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
-      "  rank_zero_warn(\n",
       "\n",
       "  | Name  | Type             | Params\n",
       "-------------------------------------------\n",
@@ -2186,7 +2181,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82f9178e94874e7e9cbb3b27cc1c351f",
+       "model_id": "6c811e724d3e4e6ca982bcf8c5e68832",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2560,69 +2555,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
-     ]
     }
    ],
    "source": [
@@ -2631,7 +2563,7 @@
     "                        logger=mnist_logger,\n",
     "                        callbacks=[ErrorTracker()])\n",
     "mnist_trainer.fit(mnist_module,\n",
-    "                  datamodule=mnist_dm)\n"
+    "                  datamodule=mnist_dm)"
    ]
   },
   {
@@ -2666,23 +2598,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "73755987",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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4dOItu73i7JL8/IoxGQMG1Ns9Ge4O6JSknJwcDRo0qM51Z2dna+DAgTW+xmEMAGgYPOlDCRqNhLcXz/L2QlYAgMDHGA3UyJuLZ/nqQlYAgMaJoNEIeHPxLIkLWQEAPMcFuxoBbwZ0VuJCVgAATxA0GgFvLp51scpTRgEAcBWHThoBb+8BAgCApwgajQADOgEAViFoBCh3L7zFgE4AgBW4jkYA8uTCW5W4eBYAwFNcsMsFgR40PL3wFgAA3uKCXQ2cNxfeAgDACgSNAOLthbcAAPA3gkYA8cWFtwAA8CeCRgDx1YW3AADwF4JGAOHCWwCAQEPQCCBceAsAEGgIGgGGC28BAAIJ19EIUFx4CwDgb570ody9NUBxJ1UAQCDg0AkAADCN5UHjhRdeUEJCgpo1a6akpCRt27btkvOeP39eCxcuVJcuXdSsWTMlJiZqw4YNfqwWAAC4w9KgsWbNGs2YMUPz58/Xjh07lJiYqJSUFB0/frzG+efMmaM//elPev755/Xll1/qwQcf1MiRI7Vz504/V+4b7t6BFQCAQGPpYNCkpCT169dPy5YtkySVl5crPj5eU6dO1aOPPlpt/g4dOmj27NmaPHmyY9odd9yhsLAw/e1vf3OpzfoyGNSbO7ACAGCFgLqp2rlz57R9+3YNHjz4v8UEBWnw4MHaunVrjcuUlZWpWbNmTtPCwsL08ccfX7KdsrIyFRcXOz2sVnkH1qr3LTl27JjS0tKUmZlpUWUAAPiWZUHjxIkTstvtioqKcpoeFRWlgoKCGpdJSUnRM888o6+++krl5eXauHGjMjMza723x+LFixUZGel4xMfH+/R9uIs7sAIAGhPLB4O6Y+nSperatauuuuoqhYSEaMqUKRo/fryCgi79NmbNmqWioiLH48iRI36suDruwAoAaEwsCxpt27ZVcHCwCgsLnaYXFhYqOjq6xmXatWun9evXq7S0VN9++60OHDigFi1aqHPnzpdsJzQ0VBEREU4PK3EHVgBAY2JZ0AgJCVGfPn2UlZXlmFZeXq6srCwlJyfXumyzZs0UGxurCxcu6M0339Tw4cPNLtdnuAMrAKAxsfTQyYwZM/Tyyy/r1Vdf1f79+zVp0iSVlpZq/PjxkqSxY8dq1qxZjvk/++wzZWZm6uuvv9bmzZs1ZMgQlZeX6ze/+Y1Vb8Ft3IEVANCYWHoJ8lGjRun777/XvHnzVFBQoN69e2vDhg2OAaJ5eXlO4y/Onj2rOXPm6Ouvv1aLFi00dOhQvfbaa2rVqpVF78B9lXdgTUtLk81mcxoUyh1YAQANDTdVs0hN19GIj4/XkiVLuI4GAKBe8qQPJWhYiDuwAgACCXdvDTDcgRUA0NAF1HU0AABAYCFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMY3nQeOGFF5SQkKBmzZopKSlJ27Ztq3X+JUuWqFu3bgoLC1N8fLwefvhhnT171k/VAgAAd1gaNNasWaMZM2Zo/vz52rFjhxITE5WSkqLjx4/XOH96eroeffRRzZ8/X/v379fy5cu1Zs0aPfbYY36uHAAAuMLSoPHMM89owoQJGj9+vHr06KGXXnpJ4eHheuWVV2qc/5NPPtENN9ygMWPGKCEhQbfddptGjx5d514QAABgDcuCxrlz57R9+3YNHjz4v8UEBWnw4MHaunVrjctcf/312r59uyNYfP3113r33Xc1dOjQS7ZTVlam4uJipwcAAPCPJlY1fOLECdntdkVFRTlNj4qK0oEDB2pcZsyYMTpx4oR+/OMfyzAMXbhwQQ8++GCth04WL16sBQsW+LR2AADgGssHg7ojJydHixYt0h//+Eft2LFDmZmZeuedd/TEE09ccplZs2apqKjI8Thy5IhPa7Lb7crJydGqVauUk5Mju93u0/UDABDILNuj0bZtWwUHB6uwsNBpemFhoaKjo2tcZu7cubrnnnv0wAMPSJJ69uyp0tJSTZw4UbNnz1ZQUPXcFBoaqtDQUN+/AUmZmZmaNm2ajh496pgWFxenpUuXKjU11ZQ2AQAIJJbt0QgJCVGfPn2UlZXlmFZeXq6srCwlJyfXuMyZM2eqhYng4GBJkmEY5hVbg8zMTKWlpTmFDEk6duyY0tLSlJmZ6dd6AACojyw9dDJjxgy9/PLLevXVV7V//35NmjRJpaWlGj9+vCRp7NixmjVrlmP+YcOG6cUXX9Tq1auVm5urjRs3au7cuRo2bJgjcPiD3W7XtGnTagw3ldOmT5/OYRQAQKNn2aETSRo1apS+//57zZs3TwUFBerdu7c2bNjgGCCal5fntAdjzpw5stlsmjNnjo4dO6Z27dpp2LBh+u1vf+vXujdv3lxtT8bFDMPQkSNHtHnzZg0cONB/hQEAUM/YDH8fc7BYcXGxIiMjVVRUpIiICI/WsWrVKo0ZM6bO+dLT0zV69GiP2gAAoL7xpA8NqLNO6ouYmBifzgcAQENF0PDAgAEDFBcXJ5vNVuPrNptN8fHxGjBggJ8rAwCgfiFoeCA4OFhLly6VpGpho/L5kiVL/DpAFQCA+oig4aHU1FRlZGQoNjbWaXpcXJwyMjK4jgYAAGIwqNfrs9vt2rx5s/Lz8xUTE6MBAwawJwMA0CB50odaenprQxAcHMwprAAAXAKHTgAAgGkIGgAAwDQEDQAAYBqCBgAAMI3bQSMhIUELFy5UXl6eGfUAAIAGxO2gMX36dGVmZqpz58669dZbtXr1apWVlZlRGwAACHAeBY1du3Zp27Zt6t69u6ZOnaqYmBhNmTJFO3bsMKNGAAAQoLy+YNf58+f1xz/+UTNnztT58+fVs2dPPfTQQxo/fvwl7wViJV9fsAsAgMbCrxfsOn/+vNatW6cVK1Zo48aN+tGPfqT7779fR48e1WOPPaYPPvhA6enpnq4eAAA0AG4HjR07dmjFihVatWqVgoKCNHbsWD377LO66qqrHPOMHDlS/fr182mhAAAg8LgdNPr166dbb71VL774okaMGKGmTZtWm6dTp0666667fFIgAAAIXG4Hja+//lodO3asdZ7mzZtrxYoVHhcFAAAaBrfPOjl+/Lg+++yzatM/++wzffHFFz4pCgAANAxuB43JkyfryJEj1aYfO3ZMkydP9klRAACgYXA7aHz55Ze67rrrqk2/9tpr9eWXX/qkKAAA0DC4HTRCQ0NVWFhYbXp+fr6aNPH4bFkAANAAuR00brvtNs2aNUtFRUWOaadOndJjjz2mW2+91afFAQCAwOb2Loinn35aN954ozp27Khrr71WkrRr1y5FRUXptdde83mBAAAgcLkdNGJjY7Vnzx69/vrr2r17t8LCwjR+/HiNHj26xmtqAACAxsujQRXNmzfXxIkTfV0LAABoYDwevfnll18qLy9P586dc5r+s5/9zOuiAABAw+DRlUFHjhypvXv3ymazqfLmr5V3arXb7b6tEAAABCy3zzqZNm2aOnXqpOPHjys8PFz//Oc/tWnTJvXt21c5OTkmlAgAAAKV23s0tm7dqg8//FBt27ZVUFCQgoKC9OMf/1iLFy/WQw89pJ07d5pRJwAACEBu79Gw2+1q2bKlJKlt27b67rvvJEkdO3bUwYMHfVsdAAAIaG7v0bjmmmu0e/duderUSUlJSXryyScVEhKiP//5z+rcubMZNQIAgADldtCYM2eOSktLJUkLFy7UT3/6Uw0YMEBt2rTRmjVrfF4gAAAIXDaj8rQRL5w8eVKtW7d2nHlSnxUXFysyMlJFRUWKiIiwuhwAAAKGJ32oW2M0zp8/ryZNmmjfvn1O0y+77LKACBkAAMC/3AoaTZs21eWXX861MgAAgEvcPutk9uzZeuyxx3Ty5Ekz6gEAAA2I24NBly1bpkOHDqlDhw7q2LGjmjdv7vT6jh07fFYcAAAIbG4HjREjRphQBgAAaIh8ctZJIOGsEwAAPGP6WScAAADucPvQSVBQUK2nsnJGCgAAqOR20Fi3bp3T8/Pnz2vnzp169dVXtWDBAp8VBgAAAp/Pxmikp6drzZo1euutt3yxOtMwRgMAAM9YOkbjRz/6kbKysny1OgAA0AD4JGj88MMPeu655xQbG+uL1QEAgAbC7TEaVW+eZhiGTp8+rfDwcP3tb3/zaXEAACCwuR00nn32WaegERQUpHbt2ikpKUmtW7f2aXEAACCwuR007r33XhPKAAAADZHbYzRWrFihtWvXVpu+du1avfrqqz4pCgAANAxuB43Fixerbdu21aa3b99eixYt8klRAACgYXA7aOTl5alTp07Vpnfs2FF5eXk+KQoAADQMbgeN9u3ba8+ePdWm7969W23atPFJUQAAoGFwO2iMHj1aDz30kLKzs2W322W32/Xhhx9q2rRpuuuuu8yoEQAABCi3zzp54okn9M033+iWW25RkyYVi5eXl2vs2LGM0QAAAE48vtfJV199pV27diksLEw9e/ZUx44dfV2bKbjXCQAAnvGkD3V7j0alrl27qmvXrp4uDgAAGgG3x2jccccd+v3vf19t+pNPPqmf//znPikKAAA0DG4HjU2bNmno0KHVpt9+++3atGmTT4oCAAANg9tBo6SkRCEhIdWmN23aVMXFxT4pCgAANAxuB42ePXtqzZo11aavXr1aPXr08ElRAACgYXB7MOjcuXOVmpqqw4cP6+abb5YkZWVlKT09XRkZGT4vEAAABC63g8awYcO0fv16LVq0SBkZGQoLC1NiYqI+/PBDXXbZZWbUCAAAApTH19GoVFxcrFWrVmn58uXavn277Ha7r2ozBdfRAADAM570oW6P0ai0adMmjRs3Th06dNAf/vAH3Xzzzfr00089XR0AAGiA3Dp0UlBQoJUrV2r58uUqLi7WnXfeqbKyMq1fv56BoAAAoBqX92gMGzZM3bp10549e7RkyRJ99913ev75582sDQAABDiX92i89957euihhzRp0iQuPQ4AAFzi8h6Njz/+WKdPn1afPn2UlJSkZcuW6cSJE2bWBgAAApzLQeNHP/qRXn75ZeXn5+uXv/ylVq9erQ4dOqi8vFwbN27U6dOnzawTAAAEIK9Obz148KCWL1+u1157TadOndKtt96qt99+25f1+RyntwIA4Bm/nt4qSd26ddOTTz6po0ePatWqVd6sCgAANEBeX7Ar0LBHAwAAz/h9jwYAAEBtCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANPUi6DxwgsvKCEhQc2aNVNSUpK2bdt2yXkHDhwom81W7fGTn/zEjxUDAABXWB401qxZoxkzZmj+/PnasWOHEhMTlZKSouPHj9c4f2ZmpvLz8x2Pffv2KTg4WD//+c/9XDkAAKiL5UHjmWee0YQJEzR+/Hj16NFDL730ksLDw/XKK6/UOP9ll12m6Ohox2Pjxo0KDw8naAAAUA9ZGjTOnTun7du3a/DgwY5pQUFBGjx4sLZu3erSOpYvX6677rpLzZs3r/H1srIyFRcXOz0AAIB/WBo0Tpw4IbvdrqioKKfpUVFRKigoqHP5bdu2ad++fXrggQcuOc/ixYsVGRnpeMTHx3tdNwAAcI3lh068sXz5cvXs2VP9+/e/5DyzZs1SUVGR43HkyBE/VggAQOPWxMrG27Ztq+DgYBUWFjpNLywsVHR0dK3LlpaWavXq1Vq4cGGt84WGhio0NNTrWgEAgPss3aMREhKiPn36KCsryzGtvLxcWVlZSk5OrnXZtWvXqqysTL/4xS/MLhMAAHjI0j0akjRjxgyNGzdOffv2Vf/+/bVkyRKVlpZq/PjxkqSxY8cqNjZWixcvdlpu+fLlGjFihNq0aWNF2QAAwAWWB41Ro0bp+++/17x581RQUKDevXtrw4YNjgGieXl5Cgpy3vFy8OBBffzxx/rHP/5hRckAAMBFNsMwDKuL8Kfi4mJFRkaqqKhIERERVpcDAEDA8KQPDeizTgAAQP1G0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAAprE8aLzwwgtKSEhQs2bNlJSUpG3bttU6/6lTpzR58mTFxMQoNDRUV155pd59910/VQsAANzRxMrG16xZoxkzZuill15SUlKSlixZopSUFB08eFDt27evNv+5c+d06623qn379srIyFBsbKy+/fZbtWrVyv/FAwCAOtkMwzCsajwpKUn9+vXTsmXLJEnl5eWKj4/X1KlT9eijj1ab/6WXXtJTTz2lAwcOqGnTpi61UVZWprKyMsfz4uJixcfHq6ioSBEREb55IwAANALFxcWKjIx0qw+17NDJuXPntH37dg0ePPi/xQQFafDgwdq6dWuNy7z99ttKTk7W5MmTFRUVpWuuuUaLFi2S3W6/ZDuLFy9WZGSk4xEfH+/z9wIAAGpmWdA4ceKE7Ha7oqKinKZHRUWpoKCgxmW+/vprZWRkyG63691339XcuXP1hz/8Qf/zP/9zyXZmzZqloqIix+PIkSM+fR8AAODSLB2j4a7y8nK1b99ef/7znxUcHKw+ffro2LFjeuqppzR//vwalwkNDVVoaKifKwUAAJKFQaNt27YKDg5WYWGh0/TCwkJFR0fXuExMTIyaNm2q4OBgx7Tu3buroKBA586dU0hIiKk1AwAA91h26CQkJER9+vRRVlaWY1p5ebmysrKUnJxc4zI33HCDDh06pPLycse0f/3rX4qJiSFkAABQD1l6HY0ZM2bo5Zdf1quvvqr9+/dr0qRJKi0t1fjx4yVJY8eO1axZsxzzT5o0SSdPntS0adP0r3/9S++8844WLVqkyZMnW/UWAABALSwdozFq1Ch9//33mjdvngoKCtS7d29t2LDBMUA0Ly9PQUH/zULx8fF6//339fDDD6tXr16KjY3VtGnTNHPmTKveAgAAqIWl19GwgifnAAMAgAC7jgYAAGj4CBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAQAATEPQAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABMQ9AAAACmIWgAAADTEDQAAIBpCBoAAMA0BA0AAGAaggYAADANQQMAAJiGoAEAAExD0AAAAKZpYnUB9ZXdbtf58+etLgM+0LRpUwUHB1tdBgA0SgSNKgzDUEFBgU6dOmV1KfChVq1aKTo6WjabzepSAKBRIWhUURky2rdvr/DwcDqmAGcYhs6cOaPjx49LkmJiYiyuCAAaF4LGRex2uyNktGnTxupy4CNhYWGSpOPHj6t9+/YcRgEAP2Iw6EUqx2SEh4dbXAl8rfIzZdwNAPgXQaMGHC5pePhMAcAaBA0AAGAagoZJ7Ha7cnJytGrVKuXk5Mhut1tdktsSEhK0ZMkSl+fPycmRzWbjjB0AgANBwwSZmZlKSEjQoEGDNGbMGA0aNEgJCQnKzMw0pT2bzVbr4/HHH/dovZ9//rkmTpzo8vzXX3+98vPzFRkZ6VF7AICGh7NOfCwzM1NpaWkyDMNp+rFjx5SWlqaMjAylpqb6tM38/HzHv9esWaN58+bp4MGDjmktWrRw/NswDNntdjVpUvdH365dO7fqCAkJUXR0tFvLAAAaNvZo1MEwDJWWlrr0KC4u1kMPPVQtZFSuR5KmTZum4uJil9ZX03pqEh0d7XhERkbKZrM5nh84cEAtW7bUe++9pz59+ig0NFQff/yxDh8+rOHDhysqKkotWrRQv3799MEHHzitt+qhE5vNpr/85S8aOXKkwsPD1bVrV7399tuO16seOlm5cqVatWql999/X927d1eLFi00ZMgQp2B04cIFPfTQQ2rVqpXatGmjmTNnaty4cRoxYoSLnxAAoD4jaNThzJkzatGihUuPyMhIHTt27JLrMgxDR48eVWRkpEvrO3PmjM/ex6OPPqrf/e532r9/v3r16qWSkhINHTpUWVlZ2rlzp4YMGaJhw4YpLy+v1vUsWLBAd955p/bs2aOhQ4fq7rvv1smTJy85/5kzZ/T000/rtdde06ZNm5SXl6dHHnnE8frvf/97vf7661qxYoW2bNmi4uJirV+/3ldvGwBgMYJGI7Fw4ULdeuut6tKliy677DIlJibql7/8pa655hp17dpVTzzxhLp06eK0h6Im9957r0aPHq0rrrhCixYtUklJibZt23bJ+c+fP6+XXnpJffv21XXXXacpU6YoKyvL8frzzz+vWbNmaeTIkbrqqqu0bNkytWrVyldvGwBgMcZo1CE8PFwlJSUuzbtp0yYNHTq0zvneffdd3XjjjS617St9+/Z1el5SUqLHH39c77zzjvLz83XhwgX98MMPde7R6NWrl+PfzZs3V0REhOPy3jUJDw9Xly5dHM9jYmIc8xcVFamwsFD9+/d3vB4cHKw+ffqovLzcrfcHAKifCBp1sNlsat68uUvz3nbbbYqLi9OxY8dqHF9hs9kUFxen2267ze+Xwa76Hh555BFt3LhRTz/9tK644gqFhYUpLS1N586dq3U9TZs2dXpus9lqDQU1ze/q2BMAQODj0IkPBQcHa+nSpZKqX4my8vmSJUvqxb02tmzZonvvvVcjR45Uz549FR0drW+++cavNURGRioqKkqff/65Y5rdbteOHTv8WgcAwDwEDR9LTU1VRkaGYmNjnabHxcWZcmqrp7p27arMzEzt2rVLu3fv1pgxYyw5XDF16lQtXrxYb731lg4ePKhp06bpP//5D5cMB4AGgkMnJkhNTdXw4cO1efNm5efnKyYmRgMGDKgXezIqPfPMM7rvvvt0/fXXq23btpo5c6aKi4v9XsfMmTNVUFCgsWPHKjg4WBMnTlRKSkq92lYAAM/ZjEZ2wLy4uFiRkZEqKipSRESE02tnz55Vbm6uOnXqpGbNmllUYeNWXl6u7t27684779QTTzzhs/Xy2QKA92rrQy+FPRqw1Lfffqt//OMfuummm1RWVqZly5YpNzdXY8aMsbo0AIAPMEYDlgoKCtLKlSvVr18/3XDDDdq7d68++OADde/e3erSAAA+wB4NWCo+Pl5btmyxugwAgEnYowEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqChlnsdiknR1q1quKn3W51RbUaOHCgpk+f7niekJCgJUuW1LqMzWbT+vXrvW7bV+sBANQ/BA0zZGZKCQnSoEHSmDEVPxMSKqabYNiwYRoyZEiNr23evFk2m0179uxxa52ff/65Jk6c6IvyHB5//HH17t272vT8/HzdfvvtPm0LAFA/EDR8LTNTSkuTjh51nn7sWMV0E8LG/fffr40bN+po1TYlrVixQn379lWvXr3cWme7du0UHh7uqxJrFR0drdDQUL+0BQDwL4JGXQxDKi117VFcLD30UMUyNa1HkqZNq5jPlfW5eL+7n/70p2rXrp1WrlzpNL2kpERr167ViBEjNHr0aMXGxio8PFw9e/bUqlWral1n1UMnX331lW688UY1a9ZMPXr00MaNG6stM3PmTF155ZUKDw9X586dNXfuXJ0/f16StHLlSi1YsEC7d++WzWaTzWZz1Fv10MnevXt18803KywsTG3atNHEiRNVUlLieP3ee+/ViBEj9PTTTysmJkZt2rTR5MmTHW0BAOoPLkFelzNnpBYtfLMuw6jY0xEZ6dr8JSVS8+Z1ztakSRONHTtWK1eu1OzZs2Wz2SRJa9euld1u1y9+8QutXbtWM2fOVEREhN555x3dc8896tKli/r371/n+svLy5WamqqoqCh99tlnKioqchrPUally5ZauXKlOnTooL1792rChAlq2bKlfvOb32jUqFHat2+fNmzYoA8++ECSFFnDdigtLVVKSoqSk5P1+eef6/jx43rggQc0ZcoUpyCVnZ2tmJgYZWdn69ChQxo1apR69+6tCRMm1Pl+AAD+wx6NBuK+++7T4cOH9dFHHzmmrVixQnfccYc6duyoRx55RL1791bnzp01depUDRkyRG+88YZL6/7ggw904MAB/fWvf1ViYqJuvPFGLVq0qNp8c+bM0fXXX6+EhAQNGzZMjzzyiKONsLAwtWjRQk2aNFF0dLSio6MVFhZWbR3p6ek6e/as/vrXv+qaa67RzTffrGXLlum1115TYWGhY77WrVtr2bJluuqqq/TTn/5UP/nJT5SVleXuZgMAmIw9GnUJD6/Ys+CKTZukoUPrnu/dd6Ubb3StbRddddVVuv766/XKK69o4MCBOnTokDZv3qyFCxfKbrdr0aJFeuONN3Ts2DGdO3dOZWVlLo/B2L9/v+Lj49WhQwfHtOTk5GrzrVmzRs8995wOHz6skpISXbhwQRERES6/h8q2EhMT1fyiPTk33HCDysvLdfDgQUVFRUmSrr76agUHBzvmiYmJ0d69e91qCwBgPvZo1MVmqzh84crjttukuLiKZS61rvj4ivlcWd+l1nMJ999/v958802dPn1aK1asUJcuXXTTTTfpqaee0tKlSzVz5kxlZ2dr165dSklJ0blz53ywgSps3bpVd999t4YOHaq///3v2rlzp2bPnu3TNi7WtGlTp+c2m03l5eWmtAUA8BxBw5eCg6WlSyv+XTUkVD5fsqRiPhPceeedCgoKUnp6uv7617/qvvvuk81m05YtWzR8+HD94he/UGJiojp37qx//etfLq+3e/fuOnLkiPLz8x3TPv30U6d5PvnkE3Xs2FGzZ89W37591bVrV3377bdO84SEhMhex/VEunfvrt27d6u0tNQxbcuWLQoKClK3bt1crhkAUD8QNHwtNVXKyJBiY52nx8VVTE9NNa3pFi1aaNSoUZo1a5by8/N17733SpK6du2qjRs36pNPPtH+/fv1y1/+0mm8Q10GDx6sK6+8UuPGjdPu3bu1efNmzZ4922merl27Ki8vT6tXr9bhw4f13HPPad26dU7zJCQkKDc3V7t27dKJEydUVlZWra27775bzZo107hx47Rv3z5lZ2dr6tSpuueeexyHTQAAgYOgYYbUVOmbb6TsbCk9veJnbq6pIaPS/fffr//85z9KSUlxjKmYM2eOrrvuOqWkpGjgwIGKjo7WiBEjXF5nUFCQ1q1bpx9++EH9+/fXAw88oN/+9rdO8/zsZz/Tww8/rClTpqh379765JNPNHfuXKd57rjjDg0ZMkSDBg1Su3btajzFNjw8XO+//75Onjypfv36KS0tTbfccouWLVvm/sYAAFjOZhguXqyhgSguLlZkZKSKioqqDVQ8e/ascnNz1alTJzVr1syiCmEGPlsA8F5tfeilsEcDAACYhqABAABMQ9AAAACmIWgAAADTEDRq0MjGxzYKfKYAYA2CxkUqrzZ55swZiyuBr1V+plWvKAoAMBf3OrlIcHCwWrVqpePHj0uquKaDzc3LgKN+MQxDZ86c0fHjx9WqVSun+6MAAMxH0KgiOjpakhxhAw1Dq1atHJ8tAMB/CBpV2Gw2xcTEqH379jp//rzV5cAHmjZtyp4MALAIQeMSgoOD6ZwAAPBSvRgM+sILLyghIUHNmjVTUlKStm3bdsl5V65cKZvN5vTgktIAANRPlgeNNWvWaMaMGZo/f7527NihxMREpaSk1DpGIiIiQvn5+Y5H1duRAwCA+sHyoPHMM89owoQJGj9+vHr06KGXXnpJ4eHheuWVVy65jM1mU3R0tOPB7cMBAKifLB2jce7cOW3fvl2zZs1yTAsKCtLgwYO1devWSy5XUlKijh07qry8XNddd50WLVqkq6++usZ5y8rKVFZW5nheVFQkqeIOdAAAwHWVfac7F0G0NGicOHFCdru92h6JqKgoHThwoMZlunXrpldeeUW9evVSUVGRnn76aV1//fX65z//qbi4uGrzL168WAsWLKg2PT4+3jdvAgCARub06dOKjIx0ad6AO+skOTlZycnJjufXX3+9unfvrj/96U964oknqs0/a9YszZgxw/G8vLxcJ0+eVJs2bXx2Ma7i4mLFx8fryJEjioiI8Mk6Gxq2Ud3YRnVjG9WNbVQ3tlHdLrWNDMPQ6dOn1aFDB5fXZWnQaNu2rYKDg1VYWOg0vbCw0OWLKzVt2lTXXnutDh06VOProaGhCg0NdZrWqlUrj+qtS0REBF/aOrCN6sY2qhvbqG5so7qxjepW0zZydU9GJUsHg4aEhKhPnz7KyspyTCsvL1dWVpbTXova2O127d27VzExMWaVCQAAPGT5oZMZM2Zo3Lhx6tu3r/r3768lS5aotLRU48ePlySNHTtWsbGxWrx4sSRp4cKF+tGPfqQrrrhCp06d0lNPPaVvv/1WDzzwgJVvAwAA1MDyoDFq1Ch9//33mjdvngoKCtS7d29t2LDBMUA0Ly9PQUH/3fHyn//8RxMmTFBBQYFat26tPn366JNPPlGPHj2segsKDQ3V/Pnzqx2iwX+xjerGNqob26hubKO6sY3q5sttZDPcOUcFAADADZZfsAsAADRcBA0AAGAaggYAADANQQMAAJiGoOED7tzmvrF5/PHHZbPZnB5XXXWV1WVZatOmTRo2bJg6dOggm82m9evXO71uGIbmzZunmJgYhYWFafDgwfrqq6+sKdYidW2je++9t9r3asiQIdYUa4HFixerX79+atmypdq3b68RI0bo4MGDTvOcPXtWkydPVps2bdSiRQvdcccd1S6O2JC5so0GDhxY7Xv04IMPWlSx/7344ovq1auX46JcycnJeu+99xyv++o7RNDwkie3uW9srr76auXn5zseH3/8sdUlWaq0tFSJiYl64YUXanz9ySef1HPPPaeXXnpJn332mZo3b66UlBSdPXvWz5Vap65tJElDhgxx+l6tWrXKjxVa66OPPtLkyZP16aefauPGjTp//rxuu+02lZaWOuZ5+OGH9b//+79au3atPvroI3333XdKTU21sGr/cmUbSdKECROcvkdPPvmkRRX7X1xcnH73u99p+/bt+uKLL3TzzTdr+PDh+uc//ynJh98hA17p37+/MXnyZMdzu91udOjQwVi8eLGFVdUf8+fPNxITE60uo96SZKxbt87xvLy83IiOjjaeeuopx7RTp04ZoaGhxqpVqyyo0HpVt5FhGMa4ceOM4cOHW1JPfXT8+HFDkvHRRx8ZhlHxnWnatKmxdu1axzz79+83JBlbt261qkxLVd1GhmEYN910kzFt2jTriqqHWrdubfzlL3/x6XeIPRpeqLzN/eDBgx3TXLnNfWPz1VdfqUOHDurcubPuvvtu5eXlWV1SvZWbm6uCggKn71RkZKSSkpL4TlWRk5Oj9u3bq1u3bpo0aZL+/e9/W12SZYqKiiRJl112mSRp+/btOn/+vNP36KqrrtLll1/eaL9HVbdRpddff11t27bVNddco1mzZunMmTNWlGc5u92u1atXq7S0VMnJyT79Dll+ZdBA5slt7hubpKQkrVy5Ut26dVN+fr4WLFigAQMGaN++fWrZsqXV5dU7BQUFklTjd6ryNVQcNklNTVWnTp10+PBhPfbYY7r99tu1detWBQcHW12eX5WXl2v69Om64YYbdM0110iq+B6FhIRUu4FkY/0e1bSNJGnMmDHq2LGjOnTooD179mjmzJk6ePCgMjMzLazWv/bu3avk5GSdPXtWLVq00Lp169SjRw/t2rXLZ98hggZMdfvttzv+3atXLyUlJaljx4564403dP/991tYGQLZXXfd5fh3z5491atXL3Xp0kU5OTm65ZZbLKzM/yZPnqx9+/Y1+rFPtbnUNpo4caLj3z179lRMTIxuueUWHT58WF26dPF3mZbo1q2bdu3apaKiImVkZGjcuHH66KOPfNoGh0684Ivb3Dc2rVq10pVXXqlDhw5ZXUq9VPm94Tvlns6dO6tt27aN7ns1ZcoU/f3vf1d2drbi4uIc06Ojo3Xu3DmdOnXKaf7G+D261DaqSVJSkiQ1qu9RSEiIrrjiCvXp00eLFy9WYmKili5d6tPvEEHDC764zX1jU1JSosOHDysmJsbqUuqlTp06KTo62uk7VVxcrM8++4zvVC2OHj2qf//7343me2UYhqZMmaJ169bpww8/VKdOnZxe79Onj5o2ber0PTp48KDy8vIazfeorm1Uk127dklSo/ke1aS8vFxlZWW+/Q75drxq47N69WojNDTUWLlypfHll18aEydONFq1amUUFBRYXVq98P/+3/8zcnJyjNzcXGPLli3G4MGDjbZt2xrHjx+3ujTLnD592ti5c6exc+dOQ5LxzDPPGDt37jS+/fZbwzAM43e/+53RqlUr46233jL27NljDB8+3OjUqZPxww8/WFy5/9S2jU6fPm088sgjxtatW43c3Fzjgw8+MK677jqja9euxtmzZ60u3S8mTZpkREZGGjk5OUZ+fr7jcebMGcc8Dz74oHH55ZcbH374ofHFF18YycnJRnJysoVV+1dd2+jQoUPGwoULjS+++MLIzc013nrrLaNz587GjTfeaHHl/vPoo48aH330kZGbm2vs2bPHePTRRw2bzWb84x//MAzDd98hgoYPPP/888bll19uhISEGP379zc+/fRTq0uqN0aNGmXExMQYISEhRmxsrDFq1Cjj0KFDVpdlqezsbENStce4ceMMw6g4xXXu3LlGVFSUERoaatxyyy3GwYMHrS3az2rbRmfOnDFuu+02o127dkbTpk2Njh07GhMmTGhU4b6mbSPJWLFihWOeH374wfjVr35ltG7d2ggPDzdGjhxp5OfnW1e0n9W1jfLy8owbb7zRuOyyy4zQ0FDjiiuuMH79618bRUVF1hbuR/fdd5/RsWNHIyQkxGjXrp1xyy23OEKGYfjuO8Rt4gEAgGkYowEAAExD0AAAAKYhaAAAANMQNAAAgGkIGgAAwDQEDQAAYBqCBgAAMA1BAwAAmIagAaBBsNlsWr9+vdVlAKiCoAHAa/fee69sNlu1x5AhQ6wuDYDFmlhdAICGYciQIVqxYoXTtNDQUIuqAVBfsEcDgE+EhoYqOjra6dG6dWtJFYc1XnzxRd1+++0KCwtT586dlZGR4bT83r17dfPNNyssLExt2rTRxIkTVVJS4jTPK6+8oquvvlqhoaGKiYnRlClTnF4/ceKERo4cqfDwcHXt2lVvv/22uW8aQJ0IGgD8Yu7cubrjjju0e/du3X333brrrru0f/9+SVJpaalSUlLUunVrff7551q7dq0++OADpyDx4osvavLkyZo4caL27t2rt99+W1dccYVTGwsWLNCdd96pPXv2aOjQobr77rt18uRJv75PAFX47oazABqrcePGGcHBwUbz5s2dHr/97W8Nw6i4ZfeDDz7otExSUpIxadIkwzAM489//rPRunVro6SkxPH6O++8YwQFBTlu/96hQwdj9uzZl6xBkjFnzhzH85KSEkOS8d577/nsfQJwH2M0APjEoEGD9OKLLzpNu+yyyxz/Tk5OdnotOTlZu3btkiTt379fiYmJat68ueP1G264QeXl5Tp48KBsNpu+++473XLLLbXW0KtXL8e/mzdvroiICB0/ftzTtwTABwgaAHyiefPm1Q5l+EpYWJhL8zVt2tTpuc1mU3l5uRklAXARYzQA+MWnn35a7Xn37t0lSd27d9fu3btVWlrqeH3Lli0KCgpSt27d1LJlSyUkJCgrK8uvNQPwHns0APhEWVmZCgoKnKY1adJEbdu2lSStXbtWffv21Y9//GO9/vrr2rZtm5YvXy5JuvvuuzV//nyNGzdOjz/+uL7//ntNnTpV99xzj6KioiRJjz/+uB588EG1b99et99+u06fPq0tW7Zo6tSp/n2jANxC0ADgExs2bFBMTIzTtG7duunAgQOSKs4IWb16tX71q18pJiZGq1atUo8ePSRJ4eHhev/99zVt2jT169dP4eHhuuOOO/TMM8841jVu3DidPXtWzz77rB555BG1bdtWaWlp/nuDADxiMwzDsLoIAA2bzWbTunXrNGLECKtLAeBnjNEAAACmIWgAAADTMEYDgOk4Qgs0XuzRAAAApiFoAAAA0xA0AACAaQgaAADANAQNAABgGoIGAAAwDUEDAACYhqABAABM8/8BLDxhn6oHWGsAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n",
     "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
@@ -2692,7 +2611,7 @@
     "             ylabel='Accuracy')\n",
     "ax.set_ylim([0.5, 1])\n",
     "ax.set_ylabel('Accuracy')\n",
-    "ax.set_xticks(np.linspace(0, 30, 7).astype(int));\n"
+    "ax.set_xticks(np.linspace(0, 30, 7).astype(int));"
    ]
   },
   {
@@ -2706,47 +2625,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "id": "f5269c40",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b584fa2b09df4c20939181c016e85bc5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "      test_accuracy         0.9681000113487244\n",
-      "        test_loss           0.14706705510616302\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'test_loss': 0.14706705510616302, 'test_accuracy': 0.9681000113487244}]"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mnist_trainer.test(mnist_module,\n",
     "                   datamodule=mnist_dm)"
@@ -2767,7 +2649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "id": "97a0b304",
    "metadata": {},
    "outputs": [],
@@ -2781,532 +2663,17 @@
     "        return self.linear(x)\n",
     "\n",
     "mlr_model = MNIST_MLR()\n",
-    "mlr_module = SimpleModule.classification(mlr_model)\n",
+    "mlr_module = SimpleModule.multiclass(mlr_model,\n",
+    "                                     num_classes=10)\n",
     "mlr_logger = CSVLogger('logs', name='MNIST_MLR')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "id": "ea685183",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (mps), used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
-      "  rank_zero_warn(\n",
-      "\n",
-      "  | Name  | Type             | Params\n",
-      "-------------------------------------------\n",
-      "0 | model | MNIST_MLR        | 7.9 K \n",
-      "1 | loss  | CrossEntropyLoss | 0     \n",
-      "-------------------------------------------\n",
-      "7.9 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "7.9 K     Total params\n",
-      "0.031     Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-       "version_minor": 0
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-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
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-     "metadata": {},
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-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
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-     "output_type": "stream",
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-      "IOPub message rate exceeded.\n",
-      "The Jupyter server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--ServerApp.iopub_msg_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
-      "ServerApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
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-     "metadata": {},
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mlr_trainer = Trainer(deterministic=True,\n",
     "                      max_epochs=30,\n",
@@ -3324,49 +2691,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "id": "c0bd63e3",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "      test_accuracy         0.9240999817848206\n",
-      "        test_loss           0.3186827003955841\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'test_loss': 0.3186827003955841, 'test_accuracy': 0.9240999817848206}]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mlr_trainer.test(mlr_module,\n",
     "                 datamodule=mnist_dm)"
@@ -3385,11 +2713,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "id": "6b0d3daa",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "del(mnist_test,\n",
@@ -3416,19 +2742,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "id": "67517b11",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n",
-      "Files already downloaded and verified\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "(cifar_train, \n",
     " cifar_test) = [CIFAR100(root=\"data\",\n",
@@ -3439,7 +2756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "id": "ee7b040e",
    "metadata": {},
    "outputs": [],
@@ -3470,18 +2787,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "id": "bd9e74ad",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "cifar_dm = SimpleDataModule(cifar_train,\n",
     "                            cifar_test,\n",
     "                            validation=0.2,\n",
     "                            num_workers=max_num_workers,\n",
-    "                            batch_size=128)\n"
+    "                            batch_size=128)"
    ]
   },
   {
@@ -3494,29 +2809,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "id": "a553b926",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X:  torch.Size([128, 3, 32, 32])\n",
-      "Y:  torch.Size([128])\n",
-      "X:  torch.Size([128, 3, 32, 32])\n",
-      "Y:  torch.Size([128])\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n",
     "    print('X: ', X_.shape)\n",
     "    print('Y: ', Y_.shape)\n",
     "    if idx >= 1:\n",
-    "        break\n"
+    "        break"
    ]
   },
   {
@@ -3533,23 +2835,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "id": "94885e68",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 25 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, axes = subplots(5, 5, figsize=(10,10))\n",
     "rng = np.random.default_rng(4)\n",
@@ -3562,7 +2851,7 @@
     "                                      [1,2,0]),\n",
     "                                      interpolation=None)\n",
     "        axes[i,j].set_xticks([])\n",
-    "        axes[i,j].set_yticks([])\n"
+    "        axes[i,j].set_yticks([])"
    ]
   },
   {
@@ -3583,7 +2872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "id": "cdc2528a",
    "metadata": {},
    "outputs": [],
@@ -3603,7 +2892,7 @@
     "        self.pool = nn.MaxPool2d(kernel_size=(2,2))\n",
     "\n",
     "    def forward(self, x):\n",
-    "        return self.pool(self.activation(self.conv(x)))\n"
+    "        return self.pool(self.activation(self.conv(x)))"
    ]
   },
   {
@@ -3628,7 +2917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "id": "845be1ae",
    "metadata": {},
    "outputs": [],
@@ -3651,7 +2940,7 @@
     "    def forward(self, x):\n",
     "        val = self.conv(x)\n",
     "        val = torch.flatten(val, start_dim=1)\n",
-    "        return self.output(val)\n"
+    "        return self.output(val)"
    ]
   },
   {
@@ -3664,69 +2953,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "id": "be768cbb",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return super().__sizeof__() + self.nbytes()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "===================================================================================================================\n",
-       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
-       "===================================================================================================================\n",
-       "CIFARModel                               [128, 3, 32, 32]          [128, 100]                --\n",
-       "├─Sequential: 1-1                        [128, 3, 32, 32]          [128, 256, 2, 2]          --\n",
-       "│    └─BuildingBlock: 2-1                [128, 3, 32, 32]          [128, 32, 16, 16]         --\n",
-       "│    │    └─Conv2d: 3-1                  [128, 3, 32, 32]          [128, 32, 32, 32]         896\n",
-       "│    │    └─ReLU: 3-2                    [128, 32, 32, 32]         [128, 32, 32, 32]         --\n",
-       "│    │    └─MaxPool2d: 3-3               [128, 32, 32, 32]         [128, 32, 16, 16]         --\n",
-       "│    └─BuildingBlock: 2-2                [128, 32, 16, 16]         [128, 64, 8, 8]           --\n",
-       "│    │    └─Conv2d: 3-4                  [128, 32, 16, 16]         [128, 64, 16, 16]         18,496\n",
-       "│    │    └─ReLU: 3-5                    [128, 64, 16, 16]         [128, 64, 16, 16]         --\n",
-       "│    │    └─MaxPool2d: 3-6               [128, 64, 16, 16]         [128, 64, 8, 8]           --\n",
-       "│    └─BuildingBlock: 2-3                [128, 64, 8, 8]           [128, 128, 4, 4]          --\n",
-       "│    │    └─Conv2d: 3-7                  [128, 64, 8, 8]           [128, 128, 8, 8]          73,856\n",
-       "│    │    └─ReLU: 3-8                    [128, 128, 8, 8]          [128, 128, 8, 8]          --\n",
-       "│    │    └─MaxPool2d: 3-9               [128, 128, 8, 8]          [128, 128, 4, 4]          --\n",
-       "│    └─BuildingBlock: 2-4                [128, 128, 4, 4]          [128, 256, 2, 2]          --\n",
-       "│    │    └─Conv2d: 3-10                 [128, 128, 4, 4]          [128, 256, 4, 4]          295,168\n",
-       "│    │    └─ReLU: 3-11                   [128, 256, 4, 4]          [128, 256, 4, 4]          --\n",
-       "│    │    └─MaxPool2d: 3-12              [128, 256, 4, 4]          [128, 256, 2, 2]          --\n",
-       "├─Sequential: 1-2                        [128, 1024]               [128, 100]                --\n",
-       "│    └─Dropout: 2-5                      [128, 1024]               [128, 1024]               --\n",
-       "│    └─Linear: 2-6                       [128, 1024]               [128, 512]                524,800\n",
-       "│    └─ReLU: 2-7                         [128, 512]                [128, 512]                --\n",
-       "│    └─Linear: 2-8                       [128, 512]                [128, 100]                51,300\n",
-       "===================================================================================================================\n",
-       "Total params: 964,516\n",
-       "Trainable params: 964,516\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (G): 2.01\n",
-       "===================================================================================================================\n",
-       "Input size (MB): 1.57\n",
-       "Forward/backward pass size (MB): 63.54\n",
-       "Params size (MB): 3.86\n",
-       "Estimated Total Size (MB): 68.97\n",
-       "==================================================================================================================="
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cifar_model = CIFARModel()\n",
     "summary(cifar_model,\n",
@@ -3766,512 +2996,36 @@
     "We saw earlier that entries of a module’s parameters are tensors. In passing\n",
     "the parameters to the optimizer we are doing more than\n",
     "simply passing arrays; part of the structure of the graph\n",
-    "is encoded in the tensors themselves.\n",
-    " "
+    "is encoded in the tensors themselves."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "id": "67e3e2d9",
    "metadata": {},
    "outputs": [],
    "source": [
     "cifar_optimizer = RMSprop(cifar_model.parameters(), lr=0.001)\n",
-    "cifar_module = SimpleModule.classification(cifar_model,\n",
+    "cifar_module = SimpleModule.multiclass(cifar_model, \n",
+    "                                       num_classes=100,\n",
     "                                    optimizer=cifar_optimizer)\n",
-    "cifar_logger = CSVLogger('logs', name='CIFAR100')\n"
+    "cifar_logger = CSVLogger('logs', name='CIFAR100')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "id": "1ea339ff",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (mps), used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "\n",
-      "  | Name  | Type             | Params\n",
-      "-------------------------------------------\n",
-      "0 | model | CIFARModel       | 964 K \n",
-      "1 | loss  | CrossEntropyLoss | 0     \n",
-      "-------------------------------------------\n",
-      "964 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "964 K     Total params\n",
-      "3.858     Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
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-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "model_id": "fdb66e85a34e49afafdf51191e247d54",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "data": {
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-    {
-     "data": {
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-       "version_major": 2,
-       "version_minor": 0
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-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cifar_trainer = Trainer(deterministic=True,\n",
     "                        max_epochs=30,\n",
     "                        logger=cifar_logger,\n",
     "                        callbacks=[ErrorTracker()])\n",
     "cifar_trainer.fit(cifar_module,\n",
-    "                  datamodule=cifar_dm)\n"
+    "                  datamodule=cifar_dm)"
    ]
   },
   {
@@ -4279,7 +3033,7 @@
    "id": "977e27d9",
    "metadata": {},
    "source": [
-    "This model takes 10 minutes or more to run and achieves about 42% accuracy on the test\n",
+    "This model can take 10 minutes or more to run and achieves about 42% accuracy on the test\n",
     "data. Although this is not terrible for 100-class data (a random\n",
     "classifier gets 1% accuracy), searching the web we see results around\n",
     "75%. Typically it takes a lot of architecture carpentry,\n",
@@ -4291,23 +3045,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "id": "0a9068d9",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Q1dXV9c41ZcoUtW/fXtOmTdNXX32lwsJCzZw5UzfffHPt7ZF4ImAAAOLH7ZZ275YKC6Xly71fS0qiGi58br/9dv3zn//U2LFja8dMPPDAA7rooos0duxYjR49Wunp6ZowYULQ75mUlKS1a9fqxx9/1PDhw3XHHXfoscce89vnF7/4he69917dfffdGjx4sDZu3Kj58+f77XPDDTfo6quvVlZWlrp37x5wqmyHDh307rvv6tChQxo2bJiys7N1xRVXaPHixaF/M6LAZppBTh5uIaqqqpSamqrKysqQB9QAALyOHTumkpIS9e3bV+3bt493c2Chxn62oVxD6cEAAACWI2AAAADLETAAAIDlCBgAAMByBAwAQNha2TyBVsGqnykBAwAQMl91yB9++CHOLYHVfBVF7REWOWMtEgBAyOx2u84880zt379fkrcmgy3Ect1ofmpqanTgwAF16NBBbdpEFhEIGACAsKSnp0tSbchAy5CUlKTevXtHHBgJGACAsNhsNmVkZKhHjx46ceJEvJsDi7Rr105JSZGPoCBgAAAiYrfbI75fj5aHQZ4AAMByBAwAAGA5AgYAALAcAQMAAFiOgAEAACxHwAAAAJYjYAAAAMsRMAAAgOUIGAAAwHIEDAAAYDkCBgAAsBwBAwAAWI6AAQAALEfAAAAAliNgAAAAyxEwAACA5QgYAADAcgQMAABgOQIGAACwHAEDAABYjoABAAAsR8AAAACWI2AAAADLETAAAIDlCBgAAMByBAwAAGA5AgYAALAcAQMAAFiOgAEAACxHwAAAAJYjYAAAAMsRMAAAgOXiHjCef/55uVwutW/fXiNGjNAnn3zS6P55eXk655xzlJKSIqfTqXvvvVfHjh2LUWsBAEAw4howVq1apVmzZmnBggXasmWLBg0apLFjx2r//v0B91++fLnmzJmjBQsWaPv27Xr55Ze1atUq3X///TFuOQAAaExcA8YzzzyjX//617r11lt13nnnacmSJerQoYNeeeWVgPtv3LhRl1xyiSZPniyXy6WrrrpKkyZNarLXAwAAxFbcAsbx48f12WefacyYMf9qTFKSxowZo02bNgU8ZtSoUfrss89qA8V3332nt956S9dee22D56murlZVVZXfAwAARFebeJ344MGDMgxDaWlpftvT0tK0Y8eOgMdMnjxZBw8e1M9+9jOZpqmTJ0/qzjvvbPQWyaJFi/Twww9b2nYAANC4uA/yDEVRUZEWLlyo//7v/9aWLVvk8Xj05ptv6tFHH23wmLlz56qysrL2UVpaGsMWAwDQOsWtB6Nbt26y2+3at2+f3/Z9+/YpPT094DHz58/XzTffrDvuuEOSdOGFF+ro0aOaPn265s2bp6Sk+nkpOTlZycnJ1n8AAADQoLj1YLRr104XX3yx1q9fX7utpqZG69ev18iRIwMe88MPP9QLEXa7XZJkmmb0GgsAAEIStx4MSZo1a5amTZumoUOHavjw4crLy9PRo0d16623SpKmTp2qXr16adGiRZKkcePG6ZlnntGQIUM0YsQI7dq1S/Pnz9e4ceNqgwYAAIi/uAaMiRMn6sCBA3rwwQdVUVGhwYMH65133qkd+Llnzx6/HosHHnhANptNDzzwgMrKytS9e3eNGzdOjz32WLw+AgAACMBmtrJ7C1VVVUpNTVVlZaU6d+4c7+YAAJAwQrmGJtQsEgAAkBgIGAAAwHIEDAAAYDkCBgAAsBwBAwAAWI6AAQAALEfAAAAAliNgAAAAyxEwAACA5QgYAADAcgQMAABgOQIGAACwHAEDAABYjoABAAAsR8AAAACWI2AAAADLETAAAIDlCBgAAMByBAwAAGA5AgYAALAcAQMAAFiOgAEAACxHwAAAAJYjYAAAAMsRMAAAgOUIGAAAwHIEDAAAYDkCBgAAsBwBAwAAWI6AAQAALEfAAAAAliNgAAAAyxEwAACA5QgYAADAcgQMAABgOQIGAACwHAEDAABYjoABAAAsR8AAAACWI2AAAADLETAAAIDlCBgAAMByBAwAAGA5AgYAALAcAQMAAFiOgAEAACxHwAAAAJYjYAAAAMsRMAAAgOUIGAAAwHIEDAAAYDkCBgAAsBwBAwAAWI6AAQAALEfAAAAAliNgAAAAyxEwAACA5QgYAADAcgQMAABgOQIGAACwHAEDAABYjoABAAAsR8AAAACWI2AAAADLETAAAIDlCBgAAMByBAwAAGA5AgYAALBcm3g3AAAAWMswDBUXF6u8vFwZGRnKzMyU3W6PaRsIGAAAtCAej0c5OTnau3dv7TaHw6H8/Hy53e6YtYNbJAAAtBAej0fZ2dl+4UKSysrKlJ2dLY/HE7O2EDAAAGgBDMNQTk6OTNOs95pvW25urgzDiEl7CBgAADRThmGoqKhIK1asUFFRUaPhoLi4uF7PxelM01RpaamKi4uj0dR6GIMBAEAzFOpYivLy8qDeN9j9IkUPBgAAzUw4YykyMjKCeu9g94uUzQx0s6YFq6qqUmpqqiorK9W5c+d4NwcAAD+GYcjlcjV4u8Nms8nhcKikpMRv6qnvuLKysoDjMBo6LhShXEPpwQAAoBkJdyyF3W5Xfn6+JG+YOJ3veV5eXszqYRAwAACIolAGakqRjaVwu90qKChQr169/LY7HA4VFBTEtA4GgzwBAIiScIpeRTqWwu12a/z48XGv5MkYDAAAosA3ULPuZdZ3u6KhHoVYjKUIF2MwAACIo0iKXjW3sRThImAAAGCxSIteNaexFOGKe8B4/vnn5XK51L59e40YMUKffPJJo/t///33mjFjhjIyMpScnKyf/vSneuutt2LUWgBAaxTLgZo+brdbu3fvVmFhoZYvX67CwkKVlJQkRLiQ4jzIc9WqVZo1a5aWLFmiESNGKC8vT2PHjtXOnTvVo0ePevsfP35cV155pXr06FGb7P72t7/pzDPPjH3jAQCtQjwGavrY7XaNHj066LY2J3Ed5DlixAgNGzZMixcvliTV1NTI6XRq5syZmjNnTr39lyxZot/97nfasWOH2rZtG9Y5GeQJAK2XYRghza5oiQM1I5EQgzyPHz+uzz77TGPGjPlXY5KSNGbMGG3atCngMW+88YZGjhypGTNmKC0tTRdccIEWLlzYaFdVdXW1qqqq/B4AgNbH4/HI5XIpKytLkydPVlZWllwuV4NLmDNQMzJxCxgHDx6UYRhKS0vz256WlqaKioqAx3z33XcqKCiQYRh66623NH/+fD399NP67W9/2+B5Fi1apNTU1NqH0+m09HMAAJq/cNb2YKBmZOI+yDMUNTU16tGjh1588UVdfPHFmjhxoubNm6clS5Y0eMzcuXNVWVlZ+ygtLY1hiwEA8RZuTwQDNSMTt0Ge3bp1k91u1759+/y279u3T+np6QGPycjIUNu2bf26lAYMGKCKigodP35c7dq1q3dMcnKykpOTrW08ACBhhNITcfqASgZqRiZuPRjt2rXTxRdfrPXr19duq6mp0fr16zVy5MiAx1xyySXatWuXampqard98803ysjICBguAAAItyciMzNTDoej3hgKH5vNJqfTqczMzIjb2BLF9RbJrFmz9NJLL+kPf/iDtm/frrvuuktHjx7VrbfeKkmaOnWq5s6dW7v/XXfdpUOHDiknJ0fffPON3nzzTS1cuFAzZsyI10cAAMRYqDUpwu2JYKBmhMw4e+6558zevXub7dq1M4cPH25+9NFHta9ddtll5rRp0/z237hxozlixAgzOTnZ7Nevn/nYY4+ZJ0+eDPp8lZWVpiSzsrLSqo8AAIiRNWvWmA6Hw5RU+3A4HOaaNWsaPObkyZOmw+EwbTab33G+h81mM51OZ4PXkkDndDqdjZ6zpQrlGspiZwCAhBBuTYrTj5Xkd3wwx0qh189oqUK5hhIwAADNnq9wVUODNYMpXBWoIqfT6VReXl6rmNVhBQJGIwgYANA8hNIrUFRUpKysrCbfs7CwsNEZG/RERCaUa2hc1yIBALROoa7vYUVNCqn1ThmNh4QqtAUASHzhVNW0qiYFYodbJACAmAl3LEVLXTws0STEYmcAgMQXak2KcNf3oCZF4iFgAADCEurqpFJkYyla++JhiYZBngCAkDVUk8I3jqKhC36kYyncbrfGjx/PTJAEwBgMAEBIIqlJwViKxMYYDABA1IQ7jkJiLEVrEnLAcLlceuSRR7Rnz55otAcA0MxFWpOCsRStQ8gBIzc3Vx6PR/369dOVV16plStXqrq6OhptAwDESCizQayoSeF2u7V7924VFhZq+fLlKiwsVElJCeGiBQl7DMaWLVu0bNkyrVixQoZhaPLkybrtttt00UUXWd1GSzEGAwD8hVpVk3EUrVdMxmBcdNFFevbZZ/X3v/9dCxYs0O9//3sNGzZMgwcP1iuvvBLwlw4A0LyEU1WTcRQIRtgB48SJE3rttdf0i1/8Qv/+7/+uoUOH6ve//71uuOEG3X///ZoyZYqV7QQAWMwwDOXk5AT8g9C3LTc3N+DtEsZRoCkh3yLZsmWLli5dqhUrVigpKUlTp07VHXfcoXPPPbd2n6+++krDhg3Tjz/+aHmDI8UtEgDwsmKFUlYnbV2iuprqsGHDdOWVV+qFF17QhAkT1LZt23r79O3bVzfddFOobw0AiCErVihldVI0JOSA8d1336lPnz6N7tOxY0ctXbo07EYBAEIXam8CK5QimkIeg7F//359/PHH9bZ//PHH+vTTTy1pFAAgNOGsC5KZmSmHw1FvoKaPzWaT0+lUZmZmtJqNFizkgDFjxgyVlpbW215WVqYZM2ZY0igAQPDCmQkiMRsE0RVywPj6668D1roYMmSIvv76a0saBQAITiQzQSRmgyB6Qh6DkZycrH379qlfv35+28vLy9WmDYuzAkAshbIuSEODMVmhFNEQciK46qqrNHfuXP35z39WamqqJOn777/X/fffryuvvNLyBgJAaxLqQE0rZoJIzAaB9UIOGE899ZQuvfRS9enTR0OGDJEkbd26VWlpafrTn/5keQMBoLUItWS3xEwQNF9hrUVy9OhRvfrqq9q2bZtSUlI0cOBATZo0KWBNjOaGQlsAmiPfQM26/0v2DbZsaDwE64IglkK5hoa92FmiImAAaG58IaGhsRRNhQRfOJHkFzKaCidAqKJaydPn66+/1p49e3T8+HG/7b/4xS/CfUsAaJUiHajpmwkS6PZKXl4e4QJxEVYlz+uvv15ffvmlbDZbbVr2JeWGpkIBAAKzYqAmM0HQ3IQcMHJyctS3b1+tX79effv21SeffKJ//OMf+vd//3c99dRT0WgjALRoVg3UZCYImpOQC21t2rRJjzzyiLp166akpCQlJSXpZz/7mRYtWqR77rknGm0EgBaNkt1oiUIOGIZhqFOnTpKkbt266e9//7skqU+fPtq5c6e1rQOABGUYhoqKirRixQoVFRU1evuYkt1oiUIOGBdccIG2bdsmSRoxYoSefPJJffjhh3rkkUfqVfcEgNYonIXHKNmNlibkaarvvvuujh49KrfbrV27dunnP/+5vvnmG3Xt2lWrVq3S5ZdfHq22WoJpqgCiKdx6Fj6hVvIEYinmdTAOHTqkLl26NHj/sDkhYACIlkjrWQDNXSjX0JBukZw4cUJt2rTRV1995bf9rLPOSohwAQDRFEo9C6ClCylgtG3bVr1796bWBQAEYNXCY0BLEPIgz3nz5un+++/XoUOHotEeAEhYLDwG/EvIYzCGDBmiXbt26cSJE+rTp486duzo9/qWLVssbaDVGIMBIFihDrhk4TG0dFFdi2TChAnhtgsAEkY4S6f76llkZ2f7LaUgUc8CrQ+rqQJAHZFONQ0UTpxOJwuPIeGxXHsjCBgAGmPVVFPqWaAliuotkqSkpEanpDLDBEBzE8rFPtKl031YeAytXcgBY+3atX7PT5w4oc8//1x/+MMf9PDDD1vWMACwQqhjKZhqClgj5IAxfvz4etuys7N1/vnna9WqVbr99tstaRgARKqhsRRlZWXKzs4OOJaCqaaANSwbg/Hdd99p4MCBOnLkiBVvFzWMwQBah3DHUjDVFGhY1EqFN+THH3/Us88+W28VQACIl3DLdrN0OmCNkG+R1F3UzDRNHT58WB06dND/+T//x9LGAUC4IhlL4Vs6PdDYDaaaAsEJOWD813/9l1/ASEpKUvfu3TVixAh16dLF0sYBQLgiHUvhdrs1fvx4ppoCYaIOBoAWibEUgPWiOgZj6dKlWr16db3tq1ev1h/+8IdQ3w4AgmIYhoqKirRixQoVFRU1WXOHsRRAfIUcMBYtWqRu3brV296jRw8tXLjQkkYBwOk8Ho9cLpeysrI0efJkZWVlyeVyyePxNHqcbyxF3QHoDoejyXLfACIT8i2S9u3ba8eOHXK5XH7bd+/erQEDBujHH3+0sn2W4xYJkFgiXRdEomw3YJWolgrv0aOHvvjii3oBY9u2beratWuobwcADTIMQzk5OQHHUJimKZvNptzcXI0fP77RwEDZbiD2Qr5FMmnSJN1zzz0qLCyUYRgyDEN//etflZOTo5tuuikabQTQSoVbywJA/IXcg/Hoo49q9+7duuKKK9SmjffwmpoaTZ06lTEYACzFuiBA4go5YLRr106rVq3Sb3/7W23dulUpKSm68MIL1adPn2i0D0ArxrogQOKiDgaAmAl1sCW1LIDmJap1MG644QY98cQT9bY/+eST+uUvfxnq2wFoJcKZakotCyBxhRwwNmzYoGuvvbbe9muuuUYbNmywpFEAWhbfVNO6AzZ9y6Y3FjKoZQEkppBvkaSkpGjr1q0655xz/Lbv2LFDQ4YMoQ4GAD/hLpse6H2oZQHEV1RvkVx44YVatWpVve0rV67UeeedF+rbAWjhrJpq6qtlMWnSJI0ePZpwATRzIc8imT9/vtxut7799ltdfvnlkqT169dr+fLlKigosLyBABIbU02B1inkgDFu3Di9/vrrWrhwoQoKCpSSkqJBgwbpr3/9q84666xotBFAMxPK7QqmmgKtU8TTVKuqqrRixQq9/PLL+uyzz5pc4TDeGIMBRMbj8SgnJ8fvtofD4VB+fn7AAZdMNQVajqiOwfDZsGGDpk2bpp49e+rpp5/W5Zdfro8++ijctwOQAMKZDcJUU6B1CilgVFRU6PHHH1f//v31y1/+Up07d1Z1dbVef/11Pf744xo2bFi02gkgzppaeEyScnNzA/ZiMtUUaH2CvkUybtw4bdiwQdddd52mTJmiq6++Wna7XW3bttW2bdsSZgYJt0iA8BQVFSkrK6vJ/QoLCxtcuZSppkBii8py7W+//bbuuece3XXXXerfv3/EjQSQWKyYDcKy6UDrEfQtkg8++ECHDx/WxRdfrBEjRmjx4sU6ePBgNNsGoBlhNgiAUAQdMP7t3/5NL730ksrLy/Wb3/xGK1euVM+ePVVTU6N169bp8OHD0WwngDjLzMyUw+GoN1DTx2azyel0KjMzM8YtA2LAMKSiImnFCu/XZj5jsjmIaJrqzp079fLLL+tPf/qTvv/+e1155ZV64403rGyf5RiDAXiFMx7CN4tEkt9gT1/oYMAmQmIYUnGxVF4uZWRImZlScxyT4/FIOTnS6bOnHA4pP19qZb/vIV1DTQucPHnSXLt2rTlu3Dgr3i6qKisrTUlmZWVlvJsCxM2aNWtMh8NhSqp9OBwOc82aNWEd63Q6gzoWqLVmjWk6HKYp/evhcHi3Nydr1pimzebfTsm7zWZrfu2NslCuoREX2ko09GCgtfP1QtT9Tz+UXghmg7RAsexN8Hik7Gzvpfp0vttvBQXNo2fAMCSXy7/n4nQ2m7cno6Skefa8REEo11ACBtCKWLWyKVqYWN4CSKSLdlGRFMTUbBUWSq1kdlRMKnkCSDxWrWyKFsTXm1D396KszLs9QHXWiBQXNxwuJG+vRmmpd794C3YBPhbqC4iAAbQirGwKP4bh7bkI1JHt25aba+2MiUS6aAc75TqaU7MTePZKyKupAkhc1LJIALEcCxFKb4JVtwCaw0U7WJmZ3ts1ZWWBQ5jvdk5TU7PD/Zkm+OwVejCAVoRaFs2cx+Mdn5CVJU2e7P3qcgV/myLUv3bj0Zvgu2g38Dsom01yOhu/aMfqr3q73Xsx97WrbjslKS+v8bAQ7s800ltXzaHnI6rzWZohpqmipTl58qRZWFhoLl++3CwsLDRPnjzZ6P5r1qwxbTababPZ/Kaa+rYx3dQiJ0+aZmGhaS5f7v3axM8l4umQ4Uz7LCysf75Aj8LC0D57U3yfte7nDeazxmN6a6BzOp3B/UzC+ZmePFn/fHWPdzob/p2K4vcolGsoAQNIYOHWs6CWRZBCDQk+of4P3ooLSiQXskDHBnNeq79HTV20rahJEW57Qz0ukp9pJMEvynU7CBiNIGCgpfD1RJweEkLpiQi15yOhhXNRCfevwHD+Bx/JBcWqcBKP3oRQfi6Rfk4r2huKSH6my5cHd+zy5f7HWfE9akLCBYzFixebffr0MZOTk83hw4ebH3/8cVDHrVixwpRkjh8/PuhzETDQEpw8ebJeD0TdkOF0Olt2aAhWOBeVWHdth3tBMU1rbnPEqzchFJF+zli3Nx4/0xjc8grlGhr3QZ6rVq3SrFmztGDBAm3ZskWDBg3S2LFjtX///kaP2717t+677z4Go6FVop5FkMIZKBfJ1M1wazxEMrPCioGabre0e7e3YNTy5d6vJSUNz1RItOmt8WhvJD/TcAfCNrMpwHEPGM8884x+/etf69Zbb9V5552nJUuWqEOHDnrllVcaPMYwDE2ZMkUPP/yw+vXrF8PWAs0D9SyCEO5FJZJCUOH+Dz6SmRVWTfu0271TUSdN8n5tbGZEPIplRfI549HeSH6m4c5eaWZTgOMaMI4fP67PPvtMY8aMqd2WlJSkMWPGaNOmTQ0e98gjj6hHjx66/fbbmzxHdXW1qqqq/B5AomuV9SxCnXYX7kUlkr8Cw/0ffCTTIa2Y9hmqRJveGo/2RjrF1e32rsnSq5f/doej4bVa4vG70Ii4BoyDBw/KMAylpaX5bU9LS1NFRUXAYz744AO9/PLLeumll4I6x6JFi5Samlr7cDqdEbcbiLdWV88inFoC4V5U4tG1LYV3QZGsqdUQqnj8pRzJ54zXX/bh/kxPPz6UW1fx+F1oTNgjPSxQVlZmSjI3btzot/0//uM/zOHDh9fbv6qqynS5XOZbb71Vu23atGmNDvI8duyYWVlZWfsoLS1lkCdahFZTzyLcwXnhDniLdOpmJLMyfOeP1bTPcFkxvTVc4XzOeLbXd/5wfqbhiuLvQsLMIqmurjbtdru5du1av+1Tp041f/GLX9Tb//PPPzclmXa7vfbh+5+p3W43d+3a1eQ5mUWClqTF17OIZNpdJBeVSENCLC/2p4vlhSzS71Ekwp12HK/2xkOUfhdCuYbGfbn2ESNGaPjw4XruueckSTU1Nerdu7fuvvtuzZkzx2/fY8eOadeuXX7bHnjgAR0+fFj5+fn66U9/qnbt2jV6PpZrR3NkGIaKi4tVXl6ujIwMZWZmBr1cetjHxnLNi3BFuly2bxaJ5D/Y09dd3Fg3daB1IJxObxdzMOtAJML3N1KRfo9iLdHa2wyFdA21JNJEYOXKlWZycrK5bNky8+uvvzanT59unnnmmWZFRYVpmqZ58803m3PmzGnw+KZukdRFDwaam3CrcUZ40tiXWzbN0P+qiqSWgE8kvQmx7tpORIn2PUq09jYzoVxD476a6sSJE3XgwAE9+OCDqqio0ODBg/XOO+/UDvzcs2ePkpLiPpsWiAqPx6Ps7GyZdToSy8rKlJ2drYKCArmt/svK91d93c5LX22IYAafhXveUFeGtGJwntstjR8fXm+Cb+omGpZo36NEa28Ci/stkljjFgmaC8Mw5HK5GiyYZbPZ5HA4VFJSEvTtkiBO6p190dD0Td/y0yUl1nbnNxRqmrpV4WtvU8tlW91eAAGFcg2lawCIk7hU44xHwaFIqig2t2l3AIJGwADiJC7VOONRcCjSUBNpLQEAcRH3MRhAaxWXapzxKDhk1VoZ4Y6jABAXBAwgTnzVOMvKyuoN8pT+NQajyWqcoUyH9FWabGpMg5UVQK1eKwNAQuAWCRAndrtd+afGF9Qt+e17npeX1/gAz1BLaMdjTEMzWx8BQGwQMIA4crvdKigoUK864wscDkfTU1TDWYrce9LYjmlgoCbQKjFNFWgGQq7GacV001hXmqSKIpDwQrmGEjAAC0VS8jskkZbQjpfWUD4baMFCuYYyyBOwiMfjUU5Ojl9tC4fDofz8fOurccZjuqkVGKgJtBqMwQAs4Cv5Xbdwlq/kt6eh8RDhisd009MZhrcXZcUK79dARbIAtGrcIgEiFNeS3/EooR3OmiIAWgRKhQMxZEnJ71B7BOI1MyPcmSsAWh0CBhChiEt+h1rLwifW000jWVMEQKtDwAAiFFHJ70h7BNxuafdu72yR5cu9X0tKonOrIh4LpQFIWMwiAeoIdapp2CW/m+oRsNm8PQLjxzd+qyNWMzMSdeYKgLigBwM4jcfjkcvlUlZWliZPnqysrCy5XK5GZ4GEXfI70XoE4j1zBUBCIWAAp/immv59715dJukmSZdJKt+7t8mppmGV/E60HgHWFAEQAqapAvrXVNNhe/cqX5LztNdKJeVK2ux0NjnVNKTbK4lYjdM3ZkTyv7XjCx3RGFwKoNmgVHgjCBgIpKioSM9mZang1PPTu/ZqTn3NlnRPYaFGW3Wxj2cti0iwpgjQalEqHAhRRVmZTlWVqHffMEnekJEnaWNZmXUn9dWyyM72holAPQLNcZVRt9s78JQ1RQA0goABSDr3wAG/2yJ1JUnqLenQgQPWnthXyyJQZczm3CPAmiIAmkDAACQN7N7dmv3CWS2UHgEALRABA5CUVLcaZjj7RbJGBz0CAFoYpqkCUu0UzIZGPJtS41MwWaMDAPwQMNAiGYahoqIirVixQkVFRTKCXDzMZrPJrFPnwbTZvAWzGhpwyRodAFAPAQMtjsfjUb8+ffRQVpbemDxZD2VlqV+fPo0WypJUO+DSVuc2iK2pxcMSrSInAMQAYzDQong8Hr16ww36QHWKZZWVKfeGG6Q1awJX1fQJZ8BlolXkBIAYIGCgxTAMQ29Pn67VAV7rJWm1pDunT9f48eMbrcYZ8oBL1ugAgHq4RYIWo7ioSA/+4x+SAhfLkqQH/vEPFRcVWXti1ugAgHoIGGjeDMO7ZseKFd6vjQyUNIqK5FTDv9S+YlmG1QHDV5FTqh8ymnNFTgCIIgIGmi+Px7tWR1aWNHmy96vL1eCUz2BvQETlRoWvImfdOhlNDRAFgBaKxc7QPPnqStT99Wxk1U5j/XrZx4xp8q2N996T/YorrGppnTcPo5InACQIVlNtBAEjAfhWGW1o6mdDq4wahn5IS1P7f/wjYNdcjaRjXbuqw759XPQBIAyhXEO5RYLmJ9y6Ena7Orz4omz61xLrPjWSbJI6vPgi4QIAYoCAgeYnkroSbrdsa9Z4i2OdxuZwyLZmDWMhACBGqIOB5ifSuhJut2x1imXZGAsBADFFwEDz46srUVYWeH0P3xiMxupKsDopAMQVt0jQ/JyqK2GaZsCxFKZpUlcCAJo5AgaaJY+kbElldbbvPbWdxc8BoHljmiqaHcMw5HK5tHfvXiVJypS3OFa5pGJ5l093OBwqKSlpfE0RAIClmKaKhFZcXKy9p6ap1kh6X9LKU199t0hKS0tVzPLnANBsETDQ7JQHOU012P0AALFHwECzkxHkNNVg9wMAxB4BA81OZmamHA6HbA0sf26z2eR0OpXJ8ucA0GwRMNDs2O125Z9a/rxuyPA9z8vLY4AnADRjBAxEn2FIRUXSihXer4bR5CFut1sFBQXqVWf5c4fDoYKCArkp+Q0AzRrTVBFdHo+Uk+O/eJnDIeXnB7UuiGEYKi4uVnl5uTIyMpSZmUnPBQDECcu1N4KAEUMej5SdLdM0dfqNDtNm8z4vKGDxMQBIINTBQPwZhpSTUy9cSJLNNL3lvnNzg7pdAgBIPAQMREdxsbR3b71w4WOTpNJS734AgBaHgIGoqCmru4pIZPsBABILAQPBC2E2yBcHDgT1lsHuBwBILAQMBMfjkVwuKStLmjzZ+9Xl8m4PYEf37iqV6i237lMjac+p/QAALQ8BA007NRvEb6qpJJWVebcHCBnpvXop59S/64YM3/PcU/sBAFoeAgYad2o2iALNZvZtCzAbJDMzU5sdDv1SUt1RFnsl/VLSp5T7BoAWi4CBxp2aDdIg0ww4G8RX7nutzaa+kkZLmnTqaz9Ja202yn0DQAtGwEDjgl0SPcB+vnLfGQ6H3pe0UtL7kno6nZT7BoAWrk28G4BmLtgl0RvYz+12a/z48ZT7BoBWhlLhaJxheGeLlJUFHodhs3nXFikpkQgNANCiUSoc1rHbvQuTSd4wcTrf87w8wgUAwA8BA01zu70Lk9WdUupwsGAZACAgxmAgOG63NH68d7ZIebl3zEVmJj0XAICACBgImiGpWFK5pAxJmZKIFwCAQAgYCIrH41FOTo72nlYTw+FwKD8/n+mmAIB6GIOBJnk8HmVnZ/uFC0kqKytTdna2PA2sRwIAaL0IGGiUYRjKyclRoNnMvm25ubkyGllZFQDQ+hAw0Kji4uJ6PRenM01TpaWlKq5TKhwA0LoRMNCo8iBLhQe7HwCgdSBgoFEZQZYKD3Y/AEDrQMBAozIzM+VwOGSrW8XzFJvNJifLrgMA6iBgoFG+Zdcl1QsZvucsuw4AqIuAgSb5ll3vVadUuMPhYNl1AEBArKaKoBmGwbLrANCKhXINpZJna2MYYa8nYrfbNXr06Oi2DwDQIhAwWhOPR8rJkU6va+FweJdj5zYHAMBCjMFoLTweKTvbP1xIUlmZdzvlvgEAFiJgtAaG4e25CDTcxrctN9e7HwAAFiBgtAbFxfV7Lk5nmlJpqXc/AAAsQMBoDYIt4025bwCARQgYrUGwZbwp9w0AsAgBoxUwRo3S3+121TTweo2kMrtdxqhRsWwWAKAFI2AkKsOQioqkFSu8XxsZoFm8caPuPvV63ZDhez7TMFS8cWM0WgoAaIWaRcB4/vnn5XK51L59e40YMUKffPJJg/u+9NJLyszMVJcuXdSlSxeNGTOm0f1bJI9HcrmkrCxp8mTvV5erwamm5eXlWispW1JZndf2ntq+Viy5DgCwTtwDxqpVqzRr1iwtWLBAW7Zs0aBBgzR27Fjt378/4P5FRUWaNGmSCgsLtWnTJjmdTl111VUqK6t76Wyhwqhn4VtKfa0kl6TRkiad+tr31PbT9wMAIFJxX4tkxIgRGjZsmBYvXixJqqmpkdPp1MyZMzVnzpwmjzcMQ126dNHixYs1derUeq9XV1erurq69nlVVZWcTmdirkViGN6eioamnNps3sqcJSV+5b8Nw5DL5VJZWZkC/bhtNpscDodKSkpYWwQA0KBQ1iKJaw/G8ePH9dlnn2nMmDG125KSkjRmzBht2rQpqPf44YcfdOLECZ111lkBX1+0aJFSU1NrH06n05K2x0WY9SxYch0AEGtxDRgHDx6UYRhKS0vz256WlqaKioqg3mP27Nnq2bOnX0g53dy5c1VZWVn7KC0tjbjdcRNBPQuWXAcAxFJCL3b2+OOPa+XKlSoqKlL79u0D7pOcnKzk5OQYtyxKIqxn4Xa7NX78eJZcBwBEXVwDRrdu3WS327Vv3z6/7fv27VN6enqjxz711FN6/PHH9d5772ngwIHRbGbzkZnpHWNRVhZ4XRHfGIzMzAbfgiXXAQCxENdbJO3atdPFF1+s9evX126rqanR+vXrNXLkyAaPe/LJJ/Xoo4/qnXfe0dChQ2PR1ObBbvcurS55w8TpfM/z8vwGeAIAEA9xn6Y6a9YsvfTSS/rDH/6g7du366677tLRo0d16623SpKmTp2quXPn1u7/xBNPaP78+XrllVfkcrlUUVGhiooKHTlyJF4fIbbcbqmgQKozlkIOh3c7YykAAM1A3MdgTJw4UQcOHNCDDz6oiooKDR48WO+8807twM89e/YoKelfOeiFF17Q8ePHlZ2d7fc+CxYs0EMPPRTLpseP2y2NH++dLVJe7h1zkZlJzwUAoNmIex2MWAtlDi8AAPiXhKmDAQAAWiYCBgAAsBwBAwAAWI6AAQAALBf3WSQIj2EYVOQEADRbBIwE5PF4lJOTo72nLXzmcDiUn5/PmiIAgGaBWyTxZBhSUZG0YoX3q2E0eYjH41F2drZfuJCksrIyZWdny+PxRKetAACEgIARLx6P5HJJWVnS5Mnery6Xd3sDDMNQTk6OApUu8W3Lzc2VEURQAQAgmggY8eDxSNnZUp1eCJWVebc3EDKKi4vr9VyczjRNlZaWqri42MrWAgAQMgJGrBmGlJMTeDVU37bc3IC3S8rLy4M6RbD7AQAQLQSMWCsurt9zcTrTlEpLvfvVkZGREdQpgt0PAIBoIWDEWrC9CwH2y8zMlMPhkK3uUu2n2Gw2OZ1OZWZmRtJCAAAiRsCItWB7FwLsZ7fblZ+fL0n1QobveV5eHvUwAABxR8CItcxMyeGQGuiFkM0mOZ3e/QJwu90qKChQr169/LY7HA4VFBRQBwMA0CywXHs8+GaRSP6DPX2ho6BAaiIoUMkTABBroVxDCRjx4vF4Z5OcPuDT6ZTy8poMFwAAxEMo11BKhceL2y2NH++dLVJe7h1zkZkp0QsBAGgBCBjxZLdLo0fHuxUAAFiOQZ4AAMByBAwAAGA5AgYAALAcAQMAAFiOgAEAACxHwAAAAJYjYAAAAMsRMAAAgOUIGAAAwHIEDAAAYDkCBgAAsBwBAwAAWI6AAQAALMdqqnFkGIaKi4tVXl6ujIwMZWZmys5y7QCAFoCAEScej0c5OTnau3dv7TaHw6H8/Hy53e44tgwAgMhxiyQOPB6PsrOz/cKFJJWVlSk7O1sejydOLQMAwBoEjBgzDEM5OTkyTbPea75tubm5Mgwj1k0DAMAyBIwYKy4urtdzcTrTNFVaWqri4uIYtgoAAGsRMGKsvLzc0v0AAGiOCBgxlpGRYel+AAA0RwSMGMvMzJTD4ZDNZgv4us1mk9PpVGZmZoxbBgCAdQgYMWa325Wfny9J9UKG73leXh71MAAACY2AEQdut1sFBQXq1auX33aHw6GCggLqYAAAEp7NDDRfsgWrqqpSamqqKisr1blz57i2hUqeAIBEEso1lEqecWS32zV69Oh4NwMAAMtxiwQAAFiOgAEAACxHwAAAAJZjDEakDEMqLpbKy6WMDCkzU2KgJgCglSNgRMLjkXJypNPXFnE4pPx8iammAIBWjFsk4fJ4pOxs/3AhSWVl3u0suQ4AaMUIGOEwDG/PRaASIr5tubne/QAAaIUIGOEoLq7fc3E605RKS737AQDQChEwwhHsUuosuQ4AaKUIGOEIdil1llwHALRSBIxwZGZ6Z4s0sOS6bDbJ6fTuBwBAK0TACIfd7p2KKtUPGb7neXnUwwAAtFoEjHC53VJBgVRnyXU5HN7t1MEAALRiFNqKhNstjR9PJU8AAOogYETKbpdYch0AAD/cIgEAAJYjYAAAAMsRMAAAgOUIGAAAwHIM8oyQYRgqLi5WeXm5MjIylJmZKTuzSAAArRwBIwIej0c5OTnae9rCZw6HQ/n5+XJTBwMA0IpxiyRMHo9H2dnZfuFCksrKypSdnS2PxxOnlgEAEH8EjDAYhqGcnByZplnvNd+23NxcGYYR66YBANAsEDDCUFxcXK/n4nSmaaq0tFTFxcUxbBUAAM0HASMM5eXllu4HAEBLQ8AIQ0ZGhqX7AQDQ0hAwwpCZmSmHwyFb3aXaT7HZbHI6ncrMzIxxywAAaB4IGGGw2+3Kz8+XpHohw/c8Ly+PehgAgFaLgBEmt9utgoIC9erVy2+7w+FQQUEBdTAAAK2azQw017IFq6qqUmpqqiorK9W5c+eI349KngCA1iKUayiVPCNkt9s1evToeDcDAIBmhVskAADAcgQMAABgOQIGAACwHAEDAABYjoABAAAsR8AAAACWI2AAAADLNYuA8fzzz8vlcql9+/YaMWKEPvnkk0b3X716tc4991y1b99eF154od56660YtRQAAAQj7gFj1apVmjVrlhYsWKAtW7Zo0KBBGjt2rPbv3x9w/40bN2rSpEm6/fbb9fnnn2vChAmaMGGCvvrqqxi3HAAANCTupcJHjBihYcOGafHixZKkmpoaOZ1OzZw5U3PmzKm3/8SJE3X06FH95S9/qd32b//2bxo8eLCWLFnS5PmsLhUOAEBrkTClwo8fP67PPvtMc+fOrd2WlJSkMWPGaNOmTQGP2bRpk2bNmuW3bezYsXr99dcD7l9dXa3q6ura55WVlZK83yQAABA837UzmL6JuAaMgwcPyjAMpaWl+W1PS0vTjh07Ah5TUVERcP+KioqA+y9atEgPP/xwve1OpzPMVgMA0LodPnxYqampje7T4hc7mzt3rl+PR01NjQ4dOqSuXbvKZrNZco6qqio5nU6VlpbG9LZLPM7LOVvWOQEgFKZp6vDhw+rZs2eT+8Y1YHTr1k12u1379u3z275v3z6lp6cHPCY9PT2k/ZOTk5WcnOy37cwzzwy/0Y3o3LlzXC4M8Tgv52xZ5wSAYDXVc+ET11kk7dq108UXX6z169fXbqupqdH69es1cuTIgMeMHDnSb39JWrduXYP7AwCA2Iv7LZJZs2Zp2rRpGjp0qIYPH668vDwdPXpUt956qyRp6tSp6tWrlxYtWiRJysnJ0WWXXaann35a1113nVauXKlPP/1UL774Yjw/BgAAOE3cA8bEiRN14MABPfjgg6qoqNDgwYP1zjvv1A7k3LNnj5KS/tXRMmrUKC1fvlwPPPCA7r//fvXv31+vv/66Lrjggnh9BCUnJ2vBggX1bsW0xPNyzpZ1TgCIlrjXwQAAAC1P3Ct5AgCAloeAAQAALEfAAAAAliNgAAAAyxEwLBDqcvORWLRokYYNG6ZOnTqpR48emjBhgnbu3Bm18wXy+OOPy2azKTc3N6rnKSsr069+9St17dpVKSkpuvDCC/Xpp59G9ZyGYWj+/Pnq27evUlJSdPbZZ+vRRx8Nqu5+sDZs2KBx48apZ8+estls9dbRMU1TDz74oDIyMpSSkqIxY8bof/7nfyw7PwDEAgEjQqEuNx+p999/XzNmzNBHH32kdevW6cSJE7rqqqt09OjRqJyvrs2bN+t//+//rYEDB0b1PP/85z91ySWXqG3btnr77bf19ddf6+mnn1aXLl2iet4nnnhCL7zwghYvXqzt27friSee0JNPPqnnnnvOsnMcPXpUgwYN0vPPPx/w9SeffFLPPvuslixZoo8//lgdO3bU2LFjdezYMcvaAABRZyIiw4cPN2fMmFH73DAMs2fPnuaiRYticv79+/ebksz3338/6uc6fPiw2b9/f3PdunXmZZddZubk5ETtXLNnzzZ/9rOfRe39G3LdddeZt912m982t9ttTpkyJSrnk2SuXbu29nlNTY2Znp5u/u53v6vd9v3335vJycnmihUrotIGAIgGejAi4FtufsyYMbXbmlpu3mq+5efPOuusqJ9rxowZuu666/w+b7S88cYbGjp0qH75y1+qR48eGjJkiF566aWon3fUqFFav369vvnmG0nStm3b9MEHH+iaa66J+rklqaSkRBUVFX7f49TUVI0YMSJmv1MAYIW4V/JMZOEsN2+lmpoa5ebm6pJLLol6JdOVK1dqy5Yt2rx5c1TP4/Pdd9/phRde0KxZs3T//fdr8+bNuueee9SuXTtNmzYtauedM2eOqqqqdO6558put8swDD322GOaMmVK1M55uoqKCkkK+Dvlew0AEgEBI4HNmDFDX331lT744IOonqe0tFQ5OTlat26d2rdvH9Vz+dTU1Gjo0KFauHChJGnIkCH66quvtGTJkqgGjNdee02vvvqqli9frvPPP19bt25Vbm6uevbsGdXzAkBLwy2SCISz3LxV7r77bv3lL39RYWGhHA5HVM/12Wefaf/+/brooovUpk0btWnTRu+//76effZZtWnTRoZhWH7OjIwMnXfeeX7bBgwYoD179lh+rtP9x3/8h+bMmaObbrpJF154oW6++Wbde++9tYvtRZvv9yYev1MAYCUCRgTCWW4+UqZp6u6779batWv117/+VX379o3KeU53xRVX6Msvv9TWrVtrH0OHDtWUKVO0detW2e12y895ySWX1Jt++80336hPnz6Wn+t0P/zwg9/iepJkt9tVU1MT1fP69O3bV+np6X6/U1VVVfr444+j9jsFANHALZIINbXcvNVmzJih5cuX689//rM6depUe18+NTVVKSkpUTlnp06d6o3x6Nixo7p27Rq1sR/33nuvRo0apYULF+rGG2/UJ598ohdffFEvvvhiVM7nM27cOD322GPq3bu3zj//fH3++ed65plndNttt1l2jiNHjmjXrl21z0tKSrR161adddZZ6t27t3Jzc/Xb3/5W/fv3V9++fTV//nz17NlTEyZMsKwNABB18Z7G0hI899xzZu/evc127dqZw4cPNz/66KOonUtSwMfSpUujds5Aoj1N1TRN8//+3/9rXnDBBWZycrJ57rnnmi+++GJUz2eapllVVWXm5OSYvXv3Ntu3b2/269fPnDdvnlldXW3ZOQoLCwP+DKdNm2aapneq6vz58820tDQzOTnZvOKKK8ydO3dadn4AiAWWawcAAJZjDAYAALAcAQMAAFiOgAEAACxHwAAAAJYjYAAAAMsRMAAAgOUIGAAAwHIEDAAAYDkCBoAWwWaz6fXXX493MwCcQsAAELFbbrlFNput3uPqq6+Od9MAxAmLnQGwxNVXX62lS5f6bUtOTo5TawDEGz0YACyRnJys9PR0v0eXLl0keW9fvPDCC7rmmmuUkpKifv36qaCgwO/4L7/8UpdffrlSUlLUtWtXTZ8+XUeOHPHb55VXXtH555+v5ORkZWRk6O677/Z7/eDBg7r++uvVoUMH9e/fX2+88UZ0PzSABhEwAMTE/PnzdcMNN2jbtm2aMmWKbrrpJm3fvl2SdPToUY0dO1ZdunTR5s2btXr1ar333nt+AeKFF17QjBkzNH36dH355Zd644039JOf/MTvHA8//LBuvPFGffHFF7r22ms1ZcoUHTp0KKafE8Ap8V7OFUDimzZtmmm3282OHTv6PR577DHTNE1TknnnnXf6HTNixAjzrrvuMk3TNF988UWzS5cu5pEjR2pff/PNN82kpCSzoqLCNE3T7Nmzpzlv3rwG2yDJfOCBB2qfHzlyxJRkvv3225Z9TgDBYwwGAEtkZWXphRde8Nt21lln1f575MiRfq+NHDlSW7dulSRt375dgwYNUseOHWtfv+SSS1RTU6OdO3fKZrPp73//u6644opG2zBw4MDaf3fs2FGdO3fW/v37w/1IACJAwABgiY4dO9a7ZWGVlJSUoPZr27at33ObzaaamppoNAlAExiDASAmPvroo3rPBwwYIEkaMGCAtm3bpqNHj9a+/uGHHyopKUnnnHOOOnXqJJfLpfXr18e0zQDCRw8GAEtUV1eroqLCb1ubNm3UrVs3SdLq1as1dOhQ/exnP9Orr76qTz75RC+//LIkacqUKVqwYIGmTZumhx56SAcOHNDMmTN18803Ky0tTZL00EMP6c4771SPHj10zTXX6PDhw/rwww81c+bM2H5QAEEhYACwxDvvvKOMjAy/beecc4527NghyTvDY+XKlfpf/+t/KSMjQytWrNB5550nSerQoYPeffdd5eTkaNiwYerQoYNuuOEGPfPMM7XvNW3aNB07dkz/9V//pfvuu0/dunVTdnZ27D4ggJDYTNM0490IAC2bzWbT2rVrNWHChHg3BUCMMAYDAABYjoABAAAsxxgMAFHHnVig9aEHAwAAWI6AAQAALEfAAAAAliNgAAAAyxEwAACA5QgYAADAcgQMAABgOQIGAACw3P8HolEDqe22hVEAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "log_path = cifar_logger.experiment.metrics_file_path\n",
     "cifar_results = pd.read_csv(log_path)\n",
@@ -4331,52 +3072,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "id": "e71eff9b",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e804034c1964ed38bbe2ca376491f54",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "      test_accuracy         0.40880000591278076\n",
-      "        test_loss           2.4764862060546875\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'test_loss': 2.4764862060546875, 'test_accuracy': 0.40880000591278076}]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cifar_trainer.test(cifar_module,\n",
-    "                   datamodule=cifar_dm)\n"
+    "                   datamodule=cifar_dm)"
    ]
   },
   {
@@ -4401,510 +3103,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "id": "58eae430",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (mps), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "\n",
-      "  | Name  | Type             | Params\n",
-      "-------------------------------------------\n",
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    "source": [
     "try:\n",
     "    for name, metric in cifar_module.metrics.items():\n",
@@ -4953,31 +3155,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "id": "638a1c8c",
-   "metadata": {
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-   "outputs": [
-    {
-     "name": "stderr",
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-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([6, 3, 224, 224])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "resize = Resize((232,232))\n",
     "crop = CenterCrop(224)\n",
@@ -5000,228 +3181,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "id": "7776ed1e",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return super().__sizeof__() + self.nbytes()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "===================================================================================================================\n",
-       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
-       "===================================================================================================================\n",
-       "ResNet                                   [6, 3, 224, 224]          [6, 1000]                 --\n",
-       "├─Conv2d: 1-1                            [6, 3, 224, 224]          [6, 64, 112, 112]         9,408\n",
-       "├─BatchNorm2d: 1-2                       [6, 64, 112, 112]         [6, 64, 112, 112]         128\n",
-       "├─ReLU: 1-3                              [6, 64, 112, 112]         [6, 64, 112, 112]         --\n",
-       "├─MaxPool2d: 1-4                         [6, 64, 112, 112]         [6, 64, 56, 56]           --\n",
-       "├─Sequential: 1-5                        [6, 64, 56, 56]           [6, 256, 56, 56]          --\n",
-       "│    └─Bottleneck: 2-1                   [6, 64, 56, 56]           [6, 256, 56, 56]          --\n",
-       "│    │    └─Conv2d: 3-1                  [6, 64, 56, 56]           [6, 64, 56, 56]           4,096\n",
-       "│    │    └─BatchNorm2d: 3-2             [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
-       "│    │    └─ReLU: 3-3                    [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
-       "│    │    └─Conv2d: 3-4                  [6, 64, 56, 56]           [6, 64, 56, 56]           36,864\n",
-       "│    │    └─BatchNorm2d: 3-5             [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
-       "│    │    └─ReLU: 3-6                    [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
-       "│    │    └─Conv2d: 3-7                  [6, 64, 56, 56]           [6, 256, 56, 56]          16,384\n",
-       "│    │    └─BatchNorm2d: 3-8             [6, 256, 56, 56]          [6, 256, 56, 56]          512\n",
-       "│    │    └─Sequential: 3-9              [6, 64, 56, 56]           [6, 256, 56, 56]          16,896\n",
-       "│    │    └─ReLU: 3-10                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
-       "│    └─Bottleneck: 2-2                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
-       "│    │    └─Conv2d: 3-11                 [6, 256, 56, 56]          [6, 64, 56, 56]           16,384\n",
-       "│    │    └─BatchNorm2d: 3-12            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
-       "│    │    └─ReLU: 3-13                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
-       "│    │    └─Conv2d: 3-14                 [6, 64, 56, 56]           [6, 64, 56, 56]           36,864\n",
-       "│    │    └─BatchNorm2d: 3-15            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
-       "│    │    └─ReLU: 3-16                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
-       "│    │    └─Conv2d: 3-17                 [6, 64, 56, 56]           [6, 256, 56, 56]          16,384\n",
-       "│    │    └─BatchNorm2d: 3-18            [6, 256, 56, 56]          [6, 256, 56, 56]          512\n",
-       "│    │    └─ReLU: 3-19                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
-       "│    └─Bottleneck: 2-3                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
-       "│    │    └─Conv2d: 3-20                 [6, 256, 56, 56]          [6, 64, 56, 56]           16,384\n",
-       "│    │    └─BatchNorm2d: 3-21            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
-       "│    │    └─ReLU: 3-22                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
-       "│    │    └─Conv2d: 3-23                 [6, 64, 56, 56]           [6, 64, 56, 56]           36,864\n",
-       "│    │    └─BatchNorm2d: 3-24            [6, 64, 56, 56]           [6, 64, 56, 56]           128\n",
-       "│    │    └─ReLU: 3-25                   [6, 64, 56, 56]           [6, 64, 56, 56]           --\n",
-       "│    │    └─Conv2d: 3-26                 [6, 64, 56, 56]           [6, 256, 56, 56]          16,384\n",
-       "│    │    └─BatchNorm2d: 3-27            [6, 256, 56, 56]          [6, 256, 56, 56]          512\n",
-       "│    │    └─ReLU: 3-28                   [6, 256, 56, 56]          [6, 256, 56, 56]          --\n",
-       "├─Sequential: 1-6                        [6, 256, 56, 56]          [6, 512, 28, 28]          --\n",
-       "│    └─Bottleneck: 2-4                   [6, 256, 56, 56]          [6, 512, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-29                 [6, 256, 56, 56]          [6, 128, 56, 56]          32,768\n",
-       "│    │    └─BatchNorm2d: 3-30            [6, 128, 56, 56]          [6, 128, 56, 56]          256\n",
-       "│    │    └─ReLU: 3-31                   [6, 128, 56, 56]          [6, 128, 56, 56]          --\n",
-       "│    │    └─Conv2d: 3-32                 [6, 128, 56, 56]          [6, 128, 28, 28]          147,456\n",
-       "│    │    └─BatchNorm2d: 3-33            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
-       "│    │    └─ReLU: 3-34                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-35                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
-       "│    │    └─BatchNorm2d: 3-36            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
-       "│    │    └─Sequential: 3-37             [6, 256, 56, 56]          [6, 512, 28, 28]          132,096\n",
-       "│    │    └─ReLU: 3-38                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
-       "│    └─Bottleneck: 2-5                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-39                 [6, 512, 28, 28]          [6, 128, 28, 28]          65,536\n",
-       "│    │    └─BatchNorm2d: 3-40            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
-       "│    │    └─ReLU: 3-41                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-42                 [6, 128, 28, 28]          [6, 128, 28, 28]          147,456\n",
-       "│    │    └─BatchNorm2d: 3-43            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
-       "│    │    └─ReLU: 3-44                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-45                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
-       "│    │    └─BatchNorm2d: 3-46            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
-       "│    │    └─ReLU: 3-47                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
-       "│    └─Bottleneck: 2-6                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-48                 [6, 512, 28, 28]          [6, 128, 28, 28]          65,536\n",
-       "│    │    └─BatchNorm2d: 3-49            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
-       "│    │    └─ReLU: 3-50                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-51                 [6, 128, 28, 28]          [6, 128, 28, 28]          147,456\n",
-       "│    │    └─BatchNorm2d: 3-52            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
-       "│    │    └─ReLU: 3-53                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-54                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
-       "│    │    └─BatchNorm2d: 3-55            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
-       "│    │    └─ReLU: 3-56                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
-       "│    └─Bottleneck: 2-7                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-57                 [6, 512, 28, 28]          [6, 128, 28, 28]          65,536\n",
-       "│    │    └─BatchNorm2d: 3-58            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
-       "│    │    └─ReLU: 3-59                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-60                 [6, 128, 28, 28]          [6, 128, 28, 28]          147,456\n",
-       "│    │    └─BatchNorm2d: 3-61            [6, 128, 28, 28]          [6, 128, 28, 28]          256\n",
-       "│    │    └─ReLU: 3-62                   [6, 128, 28, 28]          [6, 128, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-63                 [6, 128, 28, 28]          [6, 512, 28, 28]          65,536\n",
-       "│    │    └─BatchNorm2d: 3-64            [6, 512, 28, 28]          [6, 512, 28, 28]          1,024\n",
-       "│    │    └─ReLU: 3-65                   [6, 512, 28, 28]          [6, 512, 28, 28]          --\n",
-       "├─Sequential: 1-7                        [6, 512, 28, 28]          [6, 1024, 14, 14]         --\n",
-       "│    └─Bottleneck: 2-8                   [6, 512, 28, 28]          [6, 1024, 14, 14]         --\n",
-       "│    │    └─Conv2d: 3-66                 [6, 512, 28, 28]          [6, 256, 28, 28]          131,072\n",
-       "│    │    └─BatchNorm2d: 3-67            [6, 256, 28, 28]          [6, 256, 28, 28]          512\n",
-       "│    │    └─ReLU: 3-68                   [6, 256, 28, 28]          [6, 256, 28, 28]          --\n",
-       "│    │    └─Conv2d: 3-69                 [6, 256, 28, 28]          [6, 256, 14, 14]          589,824\n",
-       "│    │    └─BatchNorm2d: 3-70            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-71                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-72                 [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
-       "│    │    └─BatchNorm2d: 3-73            [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
-       "│    │    └─Sequential: 3-74             [6, 512, 28, 28]          [6, 1024, 14, 14]         526,336\n",
-       "│    │    └─ReLU: 3-75                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    └─Bottleneck: 2-9                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    │    └─Conv2d: 3-76                 [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
-       "│    │    └─BatchNorm2d: 3-77            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-78                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-79                 [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
-       "│    │    └─BatchNorm2d: 3-80            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-81                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-82                 [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
-       "│    │    └─BatchNorm2d: 3-83            [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
-       "│    │    └─ReLU: 3-84                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    └─Bottleneck: 2-10                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    │    └─Conv2d: 3-85                 [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
-       "│    │    └─BatchNorm2d: 3-86            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-87                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-88                 [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
-       "│    │    └─BatchNorm2d: 3-89            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-90                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-91                 [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
-       "│    │    └─BatchNorm2d: 3-92            [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
-       "│    │    └─ReLU: 3-93                   [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    └─Bottleneck: 2-11                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    │    └─Conv2d: 3-94                 [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
-       "│    │    └─BatchNorm2d: 3-95            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-96                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-97                 [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
-       "│    │    └─BatchNorm2d: 3-98            [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-99                   [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-100                [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
-       "│    │    └─BatchNorm2d: 3-101           [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
-       "│    │    └─ReLU: 3-102                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    └─Bottleneck: 2-12                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    │    └─Conv2d: 3-103                [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
-       "│    │    └─BatchNorm2d: 3-104           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-105                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-106                [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
-       "│    │    └─BatchNorm2d: 3-107           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-108                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-109                [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
-       "│    │    └─BatchNorm2d: 3-110           [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
-       "│    │    └─ReLU: 3-111                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    └─Bottleneck: 2-13                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "│    │    └─Conv2d: 3-112                [6, 1024, 14, 14]         [6, 256, 14, 14]          262,144\n",
-       "│    │    └─BatchNorm2d: 3-113           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-114                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-115                [6, 256, 14, 14]          [6, 256, 14, 14]          589,824\n",
-       "│    │    └─BatchNorm2d: 3-116           [6, 256, 14, 14]          [6, 256, 14, 14]          512\n",
-       "│    │    └─ReLU: 3-117                  [6, 256, 14, 14]          [6, 256, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-118                [6, 256, 14, 14]          [6, 1024, 14, 14]         262,144\n",
-       "│    │    └─BatchNorm2d: 3-119           [6, 1024, 14, 14]         [6, 1024, 14, 14]         2,048\n",
-       "│    │    └─ReLU: 3-120                  [6, 1024, 14, 14]         [6, 1024, 14, 14]         --\n",
-       "├─Sequential: 1-8                        [6, 1024, 14, 14]         [6, 2048, 7, 7]           --\n",
-       "│    └─Bottleneck: 2-14                  [6, 1024, 14, 14]         [6, 2048, 7, 7]           --\n",
-       "│    │    └─Conv2d: 3-121                [6, 1024, 14, 14]         [6, 512, 14, 14]          524,288\n",
-       "│    │    └─BatchNorm2d: 3-122           [6, 512, 14, 14]          [6, 512, 14, 14]          1,024\n",
-       "│    │    └─ReLU: 3-123                  [6, 512, 14, 14]          [6, 512, 14, 14]          --\n",
-       "│    │    └─Conv2d: 3-124                [6, 512, 14, 14]          [6, 512, 7, 7]            2,359,296\n",
-       "│    │    └─BatchNorm2d: 3-125           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
-       "│    │    └─ReLU: 3-126                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
-       "│    │    └─Conv2d: 3-127                [6, 512, 7, 7]            [6, 2048, 7, 7]           1,048,576\n",
-       "│    │    └─BatchNorm2d: 3-128           [6, 2048, 7, 7]           [6, 2048, 7, 7]           4,096\n",
-       "│    │    └─Sequential: 3-129            [6, 1024, 14, 14]         [6, 2048, 7, 7]           2,101,248\n",
-       "│    │    └─ReLU: 3-130                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
-       "│    └─Bottleneck: 2-15                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
-       "│    │    └─Conv2d: 3-131                [6, 2048, 7, 7]           [6, 512, 7, 7]            1,048,576\n",
-       "│    │    └─BatchNorm2d: 3-132           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
-       "│    │    └─ReLU: 3-133                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
-       "│    │    └─Conv2d: 3-134                [6, 512, 7, 7]            [6, 512, 7, 7]            2,359,296\n",
-       "│    │    └─BatchNorm2d: 3-135           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
-       "│    │    └─ReLU: 3-136                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
-       "│    │    └─Conv2d: 3-137                [6, 512, 7, 7]            [6, 2048, 7, 7]           1,048,576\n",
-       "│    │    └─BatchNorm2d: 3-138           [6, 2048, 7, 7]           [6, 2048, 7, 7]           4,096\n",
-       "│    │    └─ReLU: 3-139                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
-       "│    └─Bottleneck: 2-16                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
-       "│    │    └─Conv2d: 3-140                [6, 2048, 7, 7]           [6, 512, 7, 7]            1,048,576\n",
-       "│    │    └─BatchNorm2d: 3-141           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
-       "│    │    └─ReLU: 3-142                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
-       "│    │    └─Conv2d: 3-143                [6, 512, 7, 7]            [6, 512, 7, 7]            2,359,296\n",
-       "│    │    └─BatchNorm2d: 3-144           [6, 512, 7, 7]            [6, 512, 7, 7]            1,024\n",
-       "│    │    └─ReLU: 3-145                  [6, 512, 7, 7]            [6, 512, 7, 7]            --\n",
-       "│    │    └─Conv2d: 3-146                [6, 512, 7, 7]            [6, 2048, 7, 7]           1,048,576\n",
-       "│    │    └─BatchNorm2d: 3-147           [6, 2048, 7, 7]           [6, 2048, 7, 7]           4,096\n",
-       "│    │    └─ReLU: 3-148                  [6, 2048, 7, 7]           [6, 2048, 7, 7]           --\n",
-       "├─AdaptiveAvgPool2d: 1-9                 [6, 2048, 7, 7]           [6, 2048, 1, 1]           --\n",
-       "├─Linear: 1-10                           [6, 2048]                 [6, 1000]                 2,049,000\n",
-       "===================================================================================================================\n",
-       "Total params: 25,557,032\n",
-       "Trainable params: 25,557,032\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (G): 24.54\n",
-       "===================================================================================================================\n",
-       "Input size (MB): 3.61\n",
-       "Forward/backward pass size (MB): 1066.99\n",
-       "Params size (MB): 102.23\n",
-       "Estimated Total Size (MB): 1172.83\n",
-       "==================================================================================================================="
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "resnet_model = resnet50(weights=ResNet50_Weights.DEFAULT)\n",
     "summary(resnet_model,\n",
     "        input_data=imgs,\n",
     "        col_names=['input_size',\n",
     "                   'output_size',\n",
-    "                   'num_params'])\n"
+    "                   'num_params'])"
    ]
   },
   {
@@ -5234,198 +3204,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "id": "5c8e359d",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ResNet(\n",
-       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
-       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "  (relu): ReLU(inplace=True)\n",
-       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
-       "  (layer1): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (layer2): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (3): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (layer3): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (3): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (4): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (5): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (layer4): Sequential(\n",
-       "    (0): Bottleneck(\n",
-       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "      (downsample): Sequential(\n",
-       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      )\n",
-       "    )\n",
-       "    (1): Bottleneck(\n",
-       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "    (2): Bottleneck(\n",
-       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (relu): ReLU(inplace=True)\n",
-       "    )\n",
-       "  )\n",
-       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
-       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
-       ")"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "resnet_model.eval()"
    ]
@@ -5444,12 +3226,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "80f19869",
    "metadata": {},
    "outputs": [],
    "source": [
-    "img_preds = resnet_model(imgs)\n"
+    "img_preds = resnet_model(imgs)"
    ]
   },
   {
@@ -5465,13 +3247,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "6892597d",
    "metadata": {},
    "outputs": [],
    "source": [
     "img_probs = np.exp(np.asarray(img_preds.detach()))\n",
-    "img_probs /= img_probs.sum(1)[:,None]\n"
+    "img_probs /= img_probs.sum(1)[:,None]"
    ]
   },
   {
@@ -5484,7 +3266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "3eb1e1b0",
    "metadata": {},
    "outputs": [],
@@ -5494,7 +3276,7 @@
     "                           labs.items()],\n",
     "                           columns=['idx', 'label'])\n",
     "class_labels = class_labels.set_index('idx')\n",
-    "class_labels = class_labels.sort_index()\n"
+    "class_labels = class_labels.sort_index()"
    ]
   },
   {
@@ -5509,56 +3291,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "id": "0e89d251",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image: book_images/flamingo.jpg\n",
-      "            label      prob\n",
-      "0        flamingo  0.591760\n",
-      "1       spoonbill  0.012386\n",
-      "2  American_egret  0.002105\n",
-      "Image: book_images/hawk.jpg\n",
-      "            label      prob\n",
-      "0  great_grey_owl  0.287958\n",
-      "1            kite  0.039478\n",
-      "2        fountain  0.029384\n",
-      "Image: book_images/hawk_cropped.jpeg\n",
-      "    label      prob\n",
-      "0    kite  0.301841\n",
-      "1     jay  0.121659\n",
-      "2  magpie  0.015511\n",
-      "Image: book_images/huey.jpg\n",
-      "             label      prob\n",
-      "0            Lhasa  0.151153\n",
-      "1         Shih-Tzu  0.129804\n",
-      "2  Tibetan_terrier  0.102400\n",
-      "Image: book_images/kitty.jpg\n",
-      "       label      prob\n",
-      "0      tabby  0.173628\n",
-      "1  tiger_cat  0.110415\n",
-      "2    doormat  0.093447\n",
-      "Image: book_images/weaver.jpg\n",
-      "       label      prob\n",
-      "0    jacamar  0.287284\n",
-      "1  bee_eater  0.046768\n",
-      "2     bulbul  0.037507\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for i, imgfile in enumerate(imgfiles):\n",
     "    img_df = class_labels.copy()\n",
     "    img_df['prob'] = img_probs[i]\n",
     "    img_df = img_df.sort_values(by='prob', ascending=False)[:3]\n",
     "    print(f'Image: {imgfile}')\n",
-    "    print(img_df.reset_index().drop(columns=['idx']))\n"
+    "    print(img_df.reset_index().drop(columns=['idx']))"
    ]
   },
   {
@@ -5575,11 +3318,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "id": "69456669",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "del(cifar_test,\n",
@@ -5613,36 +3354,21 @@
     "* `load_sparse()`, a sparse matrix version usable by `sklearn`, since we will compare with a lasso fit;\n",
     "* `load_sequential()`, a padded\n",
     "version of the original sequence representation, limited to the last\n",
-    "500 words of each review.\n",
-    "\n"
+    "500 words of each review."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "id": "70b3ece2",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([   1,   14,   22,   16,   43,  530,  973, 1622, 1385,   65,  458,\n",
-       "       4468], dtype=int32)"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "(imdb_seq_train,\n",
     " imdb_seq_test) = load_sequential(root='data/IMDB')\n",
     "padded_sample = np.asarray(imdb_seq_train.tensors[0][0])\n",
     "sample_review = padded_sample[padded_sample > 0][:12]\n",
-    "sample_review[:12]\n"
+    "sample_review[:12]"
    ]
   },
   {
@@ -5664,21 +3390,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "id": "349b2af7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"<START> this film was just brilliant casting location scenery story direction everyone's\""
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lookup = load_lookup(root='data/IMDB')\n",
     "' '.join(lookup[i] for i in sample_review)"
@@ -5698,11 +3413,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "id": "ffa8da37",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "max_num_workers=10\n",
@@ -5712,7 +3425,7 @@
     "                           imdb_test,\n",
     "                           validation=2000,\n",
     "                           num_workers=min(6, max_num_workers),\n",
-    "                           batch_size=512)\n"
+    "                           batch_size=512)"
    ]
   },
   {
@@ -5725,11 +3438,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "id": "11eeeabb",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class IMDBModel(nn.Module):\n",
@@ -5749,7 +3460,7 @@
     "                     self.activation,\n",
     "                     self.output]:\n",
     "            val = _map(val)\n",
-    "        return torch.flatten(val)\n"
+    "        return torch.flatten(val)"
    ]
   },
   {
@@ -5762,57 +3473,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": null,
    "id": "5fe55a29",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return super().__sizeof__() + self.nbytes()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "===================================================================================================================\n",
-       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
-       "===================================================================================================================\n",
-       "IMDBModel                                [25000, 10003]            [25000]                   --\n",
-       "├─Linear: 1-1                            [25000, 10003]            [25000, 16]               160,064\n",
-       "├─ReLU: 1-2                              [25000, 16]               [25000, 16]               --\n",
-       "├─Linear: 1-3                            [25000, 16]               [25000, 16]               272\n",
-       "├─ReLU: 1-4                              [25000, 16]               [25000, 16]               --\n",
-       "├─Linear: 1-5                            [25000, 16]               [25000, 1]                17\n",
-       "===================================================================================================================\n",
-       "Total params: 160,353\n",
-       "Trainable params: 160,353\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (G): 4.01\n",
-       "===================================================================================================================\n",
-       "Input size (MB): 1000.30\n",
-       "Forward/backward pass size (MB): 6.60\n",
-       "Params size (MB): 0.64\n",
-       "Estimated Total Size (MB): 1007.54\n",
-       "==================================================================================================================="
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "imdb_model = IMDBModel(imdb_test.tensors[0].size()[1])\n",
     "summary(imdb_model,\n",
     "        input_size=imdb_test.tensors[0].size(),\n",
     "        col_names=['input_size',\n",
     "                   'output_size',\n",
-    "                   'num_params'])\n"
+    "                   'num_params'])"
    ]
   },
   {
@@ -5834,7 +3505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "id": "e1ebbc70",
    "metadata": {},
    "outputs": [],
@@ -5842,7 +3513,7 @@
     "imdb_optimizer = RMSprop(imdb_model.parameters(), lr=0.001)\n",
     "imdb_module = SimpleModule.binary_classification(\n",
     "                         imdb_model,\n",
-    "                         optimizer=imdb_optimizer)\n"
+    "                         optimizer=imdb_optimizer)"
    ]
   },
   {
@@ -5857,496 +3528,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "id": "9feddeb2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (mps), used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
-      "  rank_zero_warn(\n",
-      "\n",
-      "  | Name  | Type              | Params\n",
-      "--------------------------------------------\n",
-      "0 | model | IMDBModel         | 160 K \n",
-      "1 | loss  | BCEWithLogitsLoss | 0     \n",
-      "--------------------------------------------\n",
-      "160 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "160 K     Total params\n",
-      "0.641     Total estimated model params size (MB)\n"
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-       "Validation: 0it [00:00, ?it/s]"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "imdb_logger = CSVLogger('logs', name='IMDB')\n",
     "imdb_trainer = Trainer(deterministic=True,\n",
@@ -6367,49 +3552,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "id": "887d7dad",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "      test_accuracy         0.8543599843978882\n",
-      "        test_loss           1.1992340087890625\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'test_loss': 1.1992340087890625, 'test_accuracy': 0.8543599843978882}]"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "test_results = imdb_trainer.test(imdb_module, datamodule=imdb_dm)\n",
     "test_results"
@@ -6429,7 +3575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "id": "055a9aeb",
    "metadata": {},
    "outputs": [],
@@ -6438,7 +3584,7 @@
     " (X_valid, Y_valid),\n",
     " (X_test, Y_test)) = load_sparse(validation=2000,\n",
     "                                 random_state=0,\n",
-    "                                 root='data/IMDB')\n"
+    "                                 root='data/IMDB')"
    ]
   },
   {
@@ -6453,16 +3599,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "id": "e3e1e181",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "lam_max = np.abs(X_train.T * (Y_train - Y_train.mean())).max()\n",
     "lam_val = lam_max * np.exp(np.linspace(np.log(1),\n",
-    "                                       np.log(1e-4), 50))\n"
+    "                                       np.log(1e-4), 50))"
    ]
   },
   {
@@ -6473,23 +3617,21 @@
     "With `LogisticRegression()` the regularization parameter\n",
     "$C$ is specified as the inverse of $\\lambda$. There are several\n",
     "solvers for logistic regression; here we use `liblinear` which\n",
-    "works well with the sparse input format. "
+    "works well with the sparse input format."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": null,
    "id": "dd64350c",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "logit = LogisticRegression(penalty='l1', \n",
     "                           C=1/lam_max,\n",
     "                           solver='liblinear',\n",
     "                           warm_start=True,\n",
-    "                           fit_intercept=True)\n"
+    "                           fit_intercept=True)"
    ]
   },
   {
@@ -6502,7 +3644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": null,
    "id": "0bf8b868",
    "metadata": {},
    "outputs": [],
@@ -6514,7 +3656,7 @@
     "    logit.C = 1/l\n",
     "    logit.fit(X_train, Y_train)\n",
     "    coefs.append(logit.coef_.copy())\n",
-    "    intercepts.append(logit.intercept_)\n"
+    "    intercepts.append(logit.intercept_)"
    ]
   },
   {
@@ -6528,15 +3670,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "id": "28256828",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "coefs = np.squeeze(coefs)\n",
-    "intercepts = np.squeeze(intercepts)\n"
+    "intercepts = np.squeeze(intercepts)"
    ]
   },
   {
@@ -6550,11 +3690,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
    "id": "a5945618",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%capture\n",
@@ -6578,7 +3716,7 @@
     "                 label=data_)\n",
     "axes[0].legend()\n",
     "axes[0].set_xlabel(r'$-\\log(\\lambda)$', fontsize=20)\n",
-    "axes[0].set_ylabel('Accuracy', fontsize=20)\n"
+    "axes[0].set_ylabel('Accuracy', fontsize=20)"
    ]
   },
   {
@@ -6593,24 +3731,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "id": "74704884",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
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-      "text/plain": [
-       "<Figure size 1600x800 with 2 Axes>"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "imdb_results = pd.read_csv(imdb_logger.experiment.metrics_file_path)\n",
     "summary_plot(imdb_results,\n",
@@ -6640,11 +3764,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "id": "b1ecfca8",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "del(imdb_model,\n",
@@ -6681,12 +3803,12 @@
     "`ISLP` library. Notably, since more than 90% of the documents\n",
     "had fewer than 500 words, we set the document length to 500. For\n",
     "longer documents, we used the last 500 words, and for shorter\n",
-    "documents, we padded the front with blanks.\n"
+    "documents, we padded the front with blanks."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": null,
    "id": "5503cb3f",
    "metadata": {},
    "outputs": [],
@@ -6696,7 +3818,7 @@
     "                               validation=2000,\n",
     "                               batch_size=300,\n",
     "                               num_workers=min(6, max_num_workers)\n",
-    "                               )\n"
+    "                               )"
    ]
   },
   {
@@ -6729,11 +3851,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "id": "8b683641",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class LSTMModel(nn.Module):\n",
@@ -6760,55 +3880,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": null,
    "id": "702aa8de",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return super().__sizeof__() + self.nbytes()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "===================================================================================================================\n",
-       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
-       "===================================================================================================================\n",
-       "LSTMModel                                [10, 500]                 [10]                      --\n",
-       "├─Embedding: 1-1                         [10, 500]                 [10, 500, 32]             320,096\n",
-       "├─LSTM: 1-2                              [10, 500, 32]             [10, 500, 32]             8,448\n",
-       "├─Linear: 1-3                            [10, 32]                  [10, 1]                   33\n",
-       "===================================================================================================================\n",
-       "Total params: 328,577\n",
-       "Trainable params: 328,577\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (M): 45.44\n",
-       "===================================================================================================================\n",
-       "Input size (MB): 50.00\n",
-       "Forward/backward pass size (MB): 2.56\n",
-       "Params size (MB): 1.31\n",
-       "Estimated Total Size (MB): 53.87\n",
-       "==================================================================================================================="
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lstm_model = LSTMModel(X_test.shape[-1])\n",
     "summary(lstm_model,\n",
     "        input_data=imdb_seq_train.tensors[0][:10],\n",
     "        col_names=['input_size',\n",
     "                   'output_size',\n",
-    "                   'num_params'])\n"
+    "                   'num_params'])"
    ]
   },
   {
@@ -6822,43 +3904,603 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": null,
    "id": "e48aa272",
    "metadata": {},
    "outputs": [],
    "source": [
     "lstm_module = SimpleModule.binary_classification(lstm_model)\n",
-    "lstm_logger = CSVLogger('logs', name='IMDB_LSTM')\n"
+    "lstm_logger = CSVLogger('logs', name='IMDB_LSTM')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "id": "143be7e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstm_trainer = Trainer(deterministic=True,\n",
+    "                       max_epochs=20,\n",
+    "                       logger=lstm_logger,\n",
+    "                       callbacks=[ErrorTracker()])\n",
+    "lstm_trainer.fit(lstm_module,\n",
+    "                 datamodule=imdb_seq_dm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55dc2b66",
+   "metadata": {},
+   "source": [
+    "The rest is now similar to other networks we have fit. We\n",
+    "track the test performance as the network is fit, and see that it attains 85% accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9272e2ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eee55195",
+   "metadata": {},
+   "source": [
+    "We once again show the learning progress, followed by cleanup."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d9d387e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n",
+    "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
+    "summary_plot(lstm_results,\n",
+    "             ax,\n",
+    "             col='accuracy',\n",
+    "             ylabel='Accuracy')\n",
+    "ax.set_xticks(np.linspace(0, 20, 5).astype(int))\n",
+    "ax.set_ylabel('Accuracy')\n",
+    "ax.set_ylim([0.5, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c6d1072",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del(lstm_model,\n",
+    "    lstm_trainer,\n",
+    "    lstm_logger,\n",
+    "    imdb_seq_dm,\n",
+    "    imdb_seq_train,\n",
+    "    imdb_seq_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d179385",
+   "metadata": {},
+   "source": [
+    "### Time Series Prediction\n",
+    "We now show how to fit the models in Section 10.5.2\n",
+    "for  time series prediction.\n",
+    "We first load and standardize the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e2d1d5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NYSE = load_data('NYSE')\n",
+    "cols = ['DJ_return', 'log_volume', 'log_volatility']\n",
+    "X = pd.DataFrame(StandardScaler(\n",
+    "                     with_mean=True,\n",
+    "                     with_std=True).fit_transform(NYSE[cols]),\n",
+    "                 columns=NYSE[cols].columns,\n",
+    "                 index=NYSE.index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ad4a748",
+   "metadata": {},
+   "source": [
+    "Next we set up the lagged versions of the data, dropping\n",
+    "any rows with missing values using the `dropna()`  method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f6d0e12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for lag in range(1, 6):\n",
+    "    for col in cols:\n",
+    "        newcol = np.zeros(X.shape[0]) * np.nan\n",
+    "        newcol[lag:] = X[col].values[:-lag]\n",
+    "        X.insert(len(X.columns), \"{0}_{1}\".format(col, lag), newcol)\n",
+    "X.insert(len(X.columns), 'train', NYSE['train'])\n",
+    "X = X.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91a924d2",
+   "metadata": {},
+   "source": [
+    "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n",
+    "`log_volatility` to predict only from previous day’s data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3c961ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y, train = X['log_volume'], X['train']\n",
+    "X = X.drop(columns=['train'] + cols)\n",
+    "X.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5f8d2f",
+   "metadata": {},
+   "source": [
+    "We first fit a simple linear model and compute the $R^2$ on the test data using\n",
+    "the `score()`  method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb89da88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "M = LinearRegression()\n",
+    "M.fit(X[train], Y[train])\n",
+    "M.score(X[~train], Y[~train])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b92425ab",
+   "metadata": {},
+   "source": [
+    "We refit this model, including the factor variable `day_of_week`.\n",
+    "For a categorical series in `pandas`, we can form the indicators\n",
+    "using the `get_dummies()`  method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55d921d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_day = pd.merge(X, \n",
+    "                 pd.get_dummies(NYSE['day_of_week']),\n",
+    "                 on='date')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afab736a",
+   "metadata": {},
+   "source": [
+    " Note that we do not have\n",
+    "to reinstantiate the linear regression model\n",
+    "as its `fit()` method accepts a design matrix and a response directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e87eeb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "M.fit(X_day[train], Y[train])\n",
+    "M.score(X_day[~train], Y[~train])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43309e3b",
+   "metadata": {},
+   "source": [
+    "This model achieves an $R^2$ of about 46%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93c61d80",
+   "metadata": {},
+   "source": [
+    "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n",
+    "versions of each feature as indicated by the  `input_shape` argument\n",
+    "to the layer `nn.RNN()`  below. We first\n",
+    "ensure the columns of  our data frame are such that a reshaped\n",
+    "matrix will have the variables correctly lagged. We use the\n",
+    "`reindex()`  method to do this.\n",
+    "\n",
+    "For an input shape `(5,3)`, each row represents a lagged version of the three variables.\n",
+    "The `nn.RNN()` layer also expects the first row of each\n",
+    "observation to be earliest in time, so we must reverse the current order.  Hence we loop over\n",
+    "`range(5,0,-1)` below, which is\n",
+    "an example of using a  `slice()`  to index\n",
+    "iterable objects. The general notation is `start:end:step`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3689b318",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ordered_cols = []\n",
+    "for lag in range(5,0,-1):\n",
+    "    for col in cols:\n",
+    "        ordered_cols.append('{0}_{1}'.format(col, lag))\n",
+    "X = X.reindex(columns=ordered_cols)\n",
+    "X.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dbcf9f7",
+   "metadata": {},
+   "source": [
+    "We now reshape the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5633e521",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_rnn = X.to_numpy().reshape((-1,5,3))\n",
+    "X_rnn.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd3ae3ff",
+   "metadata": {},
+   "source": [
+    "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n",
+    "\n",
+    "Now we are ready to proceed with the RNN, which uses 12 hidden units, and  10%\n",
+    "dropout.\n",
+    "After passing through the RNN,  we extract the final time point as `val[:,-1]`\n",
+    "in `forward()` below. This gets passed through a 10% dropout and then flattened through\n",
+    "a linear layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "979a6066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NYSEModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(NYSEModel, self).__init__()\n",
+    "        self.rnn = nn.RNN(3,\n",
+    "                          12,\n",
+    "                          batch_first=True)\n",
+    "        self.dense = nn.Linear(12, 1)\n",
+    "        self.dropout = nn.Dropout(0.1)\n",
+    "    def forward(self, x):\n",
+    "        val, h_n = self.rnn(x)\n",
+    "        val = self.dense(self.dropout(val[:,-1]))\n",
+    "        return torch.flatten(val)\n",
+    "nyse_model = NYSEModel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14671ab2",
+   "metadata": {},
+   "source": [
+    "We  fit the model in a similar fashion to previous networks. We\n",
+    "supply the `fit` function with test data as validation data, so that when\n",
+    "we monitor its progress and plot the history function we can see the\n",
+    "progress on the test data. Of course we should not use this as a basis for\n",
+    "early stopping, since then the test performance would be biased.\n",
+    "\n",
+    "We form the training dataset similar to\n",
+    "our `Hitters` example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3528f4e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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",
+    "    datasets.append(TensorDataset(X_rnn_t, Y_t))\n",
+    "nyse_train, nyse_test = datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a731099",
+   "metadata": {},
+   "source": [
+    "Following our usual pattern, we inspect the summary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "795c283e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(nyse_model,\n",
+    "        input_data=X_rnn_t,\n",
+    "        col_names=['input_size',\n",
+    "                   'output_size',\n",
+    "                   'num_params'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "650e0ec7",
+   "metadata": {},
+   "source": [
+    "\\newpage\n",
+    "We again put the two datasets into a data module, with a\n",
+    "batch size of 64."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31640121",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nyse_dm = SimpleDataModule(nyse_train,\n",
+    "                           nyse_test,\n",
+    "                           num_workers=min(4, max_num_workers),\n",
+    "                           validation=nyse_test,\n",
+    "                           batch_size=64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1de7ae89",
+   "metadata": {},
+   "source": [
+    "We run some data through our model to be sure the sizes match up correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af6795f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n",
+    "    out = nyse_model(x)\n",
+    "    print(y.size(), out.size())\n",
+    "    if idx >= 2:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eecb6d9c",
+   "metadata": {},
+   "source": [
+    "We follow our previous example for setting up a trainer for a\n",
+    "regression problem, requesting the $R^2$ metric\n",
+    "to be be computed at each epoch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d04344c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nyse_optimizer = RMSprop(nyse_model.parameters(),\n",
+    "                         lr=0.001)\n",
+    "nyse_module = SimpleModule.regression(nyse_model,\n",
+    "                                      optimizer=nyse_optimizer,\n",
+    "                                      metrics={'r2':R2Score()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a10444d",
+   "metadata": {},
+   "source": [
+    "Fitting the model should by now be familiar.\n",
+    "The results on the test data are very similar to the linear AR model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "485d7bb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nyse_trainer = Trainer(deterministic=True,\n",
+    "                       max_epochs=200,\n",
+    "                       callbacks=[ErrorTracker()])\n",
+    "nyse_trainer.fit(nyse_module,\n",
+    "                 datamodule=nyse_dm)\n",
+    "nyse_trainer.test(nyse_module,\n",
+    "                  datamodule=nyse_dm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbd13ee3",
+   "metadata": {},
+   "source": [
+    "We could also fit a model without the `nn.RNN()` layer by just\n",
+    "using a `nn.Flatten()` layer instead. This would be a nonlinear AR model. If in addition we excluded the \n",
+    "hidden layer, this would be equivalent to our earlier linear AR model.  \n",
+    "\n",
+    "Instead we will fit a nonlinear AR model using the feature set `X_day` that includes the `day_of_week` indicators.\n",
+    "To do so, we\n",
+    "must first create our test and training datasets and a corresponding\n",
+    "data module. This may seem a little burdensome, but is part of the\n",
+    "general pipeline for `torch`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91d6633a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = []\n",
+    "for mask in [train, ~train]:\n",
+    "    X_day_t = torch.tensor(\n",
+    "                   np.asarray(X_day[mask]).astype(np.float32))\n",
+    "    Y_t = torch.tensor(np.asarray(Y[mask]).astype(np.float32))\n",
+    "    datasets.append(TensorDataset(X_day_t, Y_t))\n",
+    "day_train, day_test = datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5cf941c",
+   "metadata": {},
+   "source": [
+    "Creating a data module follows a familiar pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87137503",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "day_dm = SimpleDataModule(day_train,\n",
+    "                          day_test,\n",
+    "                          num_workers=min(4, max_num_workers),\n",
+    "                          validation=day_test,\n",
+    "                          batch_size=64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2575f374",
+   "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cf09a55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NonLinearARModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(NonLinearARModel, self).__init__()\n",
+    "        self._forward = nn.Sequential(nn.Flatten(),\n",
+    "                                      nn.Linear(20, 32),\n",
+    "                                      nn.ReLU(),\n",
+    "                                      nn.Dropout(0.5),\n",
+    "                                      nn.Linear(32, 1))\n",
+    "    def forward(self, x):\n",
+    "        return torch.flatten(self._forward(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "930576f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nl_model = NonLinearARModel()\n",
+    "nl_optimizer = RMSprop(nl_model.parameters(),\n",
+    "                           lr=0.001)\n",
+    "nl_module = SimpleModule.regression(nl_model,\n",
+    "                                        optimizer=nl_optimizer,\n",
+    "                                        metrics={'r2':R2Score()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0e3043c",
+   "metadata": {},
+   "source": [
+    "We continue with the usual training steps, fit the model,\n",
+    "and evaluate the test error. We see the test $R^2$ is a slight improvement over the linear AR model that also includes `day_of_week`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9ae8d0e",
    "metadata": {
-    "lines_to_next_cell": 0
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "GPU available: True (mps), used: False\n",
+      "GPU available: True (mps), used: True\n",
       "TPU available: False, using: 0 TPU cores\n",
       "IPU available: False, using: 0 IPUs\n",
       "HPU available: False, using: 0 HPUs\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
-      "  rank_zero_warn(\n",
       "\n",
-      "  | Name  | Type              | Params\n",
-      "--------------------------------------------\n",
-      "0 | model | LSTMModel         | 328 K \n",
-      "1 | loss  | BCEWithLogitsLoss | 0     \n",
-      "--------------------------------------------\n",
-      "328 K     Trainable params\n",
+      "  | Name  | Type             | Params\n",
+      "-------------------------------------------\n",
+      "0 | model | NonLinearARModel | 705   \n",
+      "1 | loss  | MSELoss          | 0     \n",
+      "-------------------------------------------\n",
+      "705       Trainable params\n",
       "0         Non-trainable params\n",
-      "328 K     Total params\n",
-      "1.314     Total estimated model params size (MB)\n"
+      "705       Total params\n",
+      "0.003     Total estimated model params size (MB)\n"
      ]
     },
     {
@@ -6878,7 +4520,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1ee0aabd8caf403c93b8c4ba0a1c2507",
+       "model_id": "49a11b07bdd648fe98b855178b3ada60",
        "version_major": 2,
        "version_minor": 0
       },
@@ -7175,36 +4817,11 @@
      "text": [
       "`Trainer.fit` stopped: `max_epochs=20` reached.\n"
      ]
-    }
-   ],
-   "source": [
-    "lstm_trainer = Trainer(deterministic=True,\n",
-    "                       max_epochs=20,\n",
-    "                       logger=lstm_logger,\n",
-    "                       callbacks=[ErrorTracker()])\n",
-    "lstm_trainer.fit(lstm_module,\n",
-    "                 datamodule=imdb_seq_dm)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55dc2b66",
-   "metadata": {},
-   "source": [
-    "The rest is now similar to other networks we have fit. We\n",
-    "track the test performance as the network is fit, and see that it attains 85% accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "9272e2ba",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "92a1cacbb1944d74b3f3c0670eb84b63",
+       "model_id": "a31407a5ed78444ea0cd603135a34997",
        "version_major": 2,
        "version_minor": 0
       },
@@ -7220,2835 +4837,17 @@
      "output_type": "stream",
      "text": [
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
+      "Runningstage.testing metric      DataLoader 0\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "      test_accuracy         0.8452399969100952\n",
-      "        test_loss           0.7559056878089905\n",
+      "        test_loss           0.5651810765266418\n",
+      "         test_r2            0.4636155962944031\n",
       "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[{'test_loss': 0.7559056878089905, 'test_accuracy': 0.8452399969100952}]"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lstm_trainer.test(lstm_module, datamodule=imdb_seq_dm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eee55195",
-   "metadata": {},
-   "source": [
-    "We once again show the learning progress, followed by cleanup."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "5d9d387e",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.5, 1.0)"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lstm_results = pd.read_csv(lstm_logger.experiment.metrics_file_path)\n",
-    "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
-    "summary_plot(lstm_results,\n",
-    "             ax,\n",
-    "             col='accuracy',\n",
-    "             ylabel='Accuracy')\n",
-    "ax.set_xticks(np.linspace(0, 20, 5).astype(int))\n",
-    "ax.set_ylabel('Accuracy')\n",
-    "ax.set_ylim([0.5, 1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "7c6d1072",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "del(lstm_model,\n",
-    "    lstm_trainer,\n",
-    "    lstm_logger,\n",
-    "    imdb_seq_dm,\n",
-    "    imdb_seq_train,\n",
-    "    imdb_seq_test)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d179385",
-   "metadata": {},
-   "source": [
-    "### Time Series Prediction\n",
-    "We now show how to fit the models in Section 10.5.2\n",
-    "for  time series prediction.\n",
-    "We first load and standardize the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "8e2d1d5c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NYSE = load_data('NYSE')\n",
-    "cols = ['DJ_return', 'log_volume', 'log_volatility']\n",
-    "X = pd.DataFrame(StandardScaler(\n",
-    "                     with_mean=True,\n",
-    "                     with_std=True).fit_transform(NYSE[cols]),\n",
-    "                 columns=NYSE[cols].columns,\n",
-    "                 index=NYSE.index)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ad4a748",
-   "metadata": {},
-   "source": [
-    "Next we set up the lagged versions of the data, dropping\n",
-    "any rows with missing values using the `dropna()`  method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "6f6d0e12",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for lag in range(1, 6):\n",
-    "    for col in cols:\n",
-    "        newcol = np.zeros(X.shape[0]) * np.nan\n",
-    "        newcol[lag:] = X[col].values[:-lag]\n",
-    "        X.insert(len(X.columns), \"{0}_{1}\".format(col, lag), newcol)\n",
-    "X.insert(len(X.columns), 'train', NYSE['train'])\n",
-    "X = X.dropna()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91a924d2",
-   "metadata": {},
-   "source": [
-    "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n",
-    "`log_volatility` to predict only from previous day’s data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "d3c961ec",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['DJ_return_1', 'log_volume_1', 'log_volatility_1', 'DJ_return_2',\n",
-       "       'log_volume_2', 'log_volatility_2', 'DJ_return_3', 'log_volume_3',\n",
-       "       'log_volatility_3', 'DJ_return_4', 'log_volume_4', 'log_volatility_4',\n",
-       "       'DJ_return_5', 'log_volume_5', 'log_volatility_5'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y, train = X['log_volume'], X['train']\n",
-    "X = X.drop(columns=['train'] + cols)\n",
-    "X.columns\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c5f8d2f",
-   "metadata": {},
-   "source": [
-    "We first fit a simple linear model and compute the $R^2$ on the test data using\n",
-    "the `score()`  method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "cb89da88",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4128912938562521"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "M = LinearRegression()\n",
-    "M.fit(X[train], Y[train])\n",
-    "M.score(X[~train], Y[~train])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b92425ab",
-   "metadata": {},
-   "source": [
-    "We refit this model, including the factor variable `day_of_week`.\n",
-    "For a categorical series in `pandas`, we can form the indicators\n",
-    "using the `get_dummies()`  method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "55d921d7",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "X_day = pd.merge(X, \n",
-    "                 pd.get_dummies(NYSE['day_of_week']),\n",
-    "                 on='date')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afab736a",
-   "metadata": {},
-   "source": [
-    " Note that we do not have\n",
-    "to reinstantiate the linear regression model\n",
-    "as its `fit()` method accepts a design matrix and a response directly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "4e87eeb9",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.45633025715446285"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "M.fit(X_day[train], Y[train])\n",
-    "M.score(X_day[~train], Y[~train])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43309e3b",
-   "metadata": {},
-   "source": [
-    "This model achieves an $R^2$ of about 46%."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93c61d80",
-   "metadata": {},
-   "source": [
-    "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n",
-    "versions of each feature as indicated by the  `input_shape` argument\n",
-    "to the layer `nn.RNN()`  below. We first\n",
-    "ensure the columns of  our data frame are such that a reshaped\n",
-    "matrix will have the variables correctly lagged. We use the\n",
-    "`reindex()`  method to do this.\n",
-    "\n",
-    "For an input shape `(5,3)`, each row represents a lagged version of the three variables.\n",
-    "The `nn.RNN()` layer also expects the first row of each\n",
-    "observation to be earliest in time, so we must reverse the current order.  Hence we loop over\n",
-    "`range(5,0,-1)` below, which is\n",
-    "an example of using a  `slice()`  to index\n",
-    "iterable objects. The general notation is `start:end:step`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "3689b318",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['DJ_return_5', 'log_volume_5', 'log_volatility_5', 'DJ_return_4',\n",
-       "       'log_volume_4', 'log_volatility_4', 'DJ_return_3', 'log_volume_3',\n",
-       "       'log_volatility_3', 'DJ_return_2', 'log_volume_2', 'log_volatility_2',\n",
-       "       'DJ_return_1', 'log_volume_1', 'log_volatility_1'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ordered_cols = []\n",
-    "for lag in range(5,0,-1):\n",
-    "    for col in cols:\n",
-    "        ordered_cols.append('{0}_{1}'.format(col, lag))\n",
-    "X = X.reindex(columns=ordered_cols)\n",
-    "X.columns\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9dbcf9f7",
-   "metadata": {},
-   "source": [
-    "We now reshape the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "5633e521",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(6046, 5, 3)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_rnn = X.to_numpy().reshape((-1,5,3))\n",
-    "X_rnn.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd3ae3ff",
-   "metadata": {},
-   "source": [
-    "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n",
-    "\n",
-    "Now we are ready to proceed with the RNN, which uses 12 hidden units, and  10%\n",
-    "dropout.\n",
-    "After passing through the RNN,  we extract the final time point as `val[:,-1]`\n",
-    "in `forward()` below. This gets passed through a 10% dropout and then flattened through\n",
-    "a linear layer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "979a6066",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NYSEModel(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(NYSEModel, self).__init__()\n",
-    "        self.rnn = nn.RNN(3,\n",
-    "                          12,\n",
-    "                          batch_first=True)\n",
-    "        self.dense = nn.Linear(12, 1)\n",
-    "        self.dropout = nn.Dropout(0.1)\n",
-    "    def forward(self, x):\n",
-    "        val, h_n = self.rnn(x)\n",
-    "        val = self.dense(self.dropout(val[:,-1]))\n",
-    "        return torch.flatten(val)\n",
-    "nyse_model = NYSEModel()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14671ab2",
-   "metadata": {},
-   "source": [
-    "We  fit the model in a similar fashion to previous networks. We\n",
-    "supply the `fit` function with test data as validation data, so that when\n",
-    "we monitor its progress and plot the history function we can see the\n",
-    "progress on the test data. Of course we should not use this as a basis for\n",
-    "early stopping, since then the test performance would be biased.\n",
-    "\n",
-    "We form the training dataset similar to\n",
-    "our `Hitters` example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "id": "3528f4e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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",
-    "    datasets.append(TensorDataset(X_rnn_t, Y_t))\n",
-    "nyse_train, nyse_test = datasets\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a731099",
-   "metadata": {},
-   "source": [
-    "Following our usual pattern, we inspect the summary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "795c283e",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
-      "  return super().__sizeof__() + self.nbytes()\n"
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-     "data": {
-      "text/plain": [
-       "===================================================================================================================\n",
-       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
-       "===================================================================================================================\n",
-       "NYSEModel                                [1770, 5, 3]              [1770]                    --\n",
-       "├─RNN: 1-1                               [1770, 5, 3]              [1770, 5, 12]             204\n",
-       "├─Dropout: 1-2                           [1770, 12]                [1770, 12]                --\n",
-       "├─Linear: 1-3                            [1770, 12]                [1770, 1]                 13\n",
-       "===================================================================================================================\n",
-       "Total params: 217\n",
-       "Trainable params: 217\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (M): 1.83\n",
-       "===================================================================================================================\n",
-       "Input size (MB): 0.11\n",
-       "Forward/backward pass size (MB): 0.86\n",
-       "Params size (MB): 0.00\n",
-       "Estimated Total Size (MB): 0.97\n",
-       "==================================================================================================================="
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary(nyse_model,\n",
-    "        input_data=X_rnn_t,\n",
-    "        col_names=['input_size',\n",
-    "                   'output_size',\n",
-    "                   'num_params'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "650e0ec7",
-   "metadata": {},
-   "source": [
-    "\\newpage\n",
-    "We again put the two datasets into a data module, with a\n",
-    "batch size of 64."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "id": "31640121",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "nyse_dm = SimpleDataModule(nyse_train,\n",
-    "                           nyse_test,\n",
-    "                           num_workers=min(4, max_num_workers),\n",
-    "                           validation=nyse_test,\n",
-    "                           batch_size=64)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1de7ae89",
-   "metadata": {},
-   "source": [
-    "We run some data through our model to be sure the sizes match up correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "id": "af6795f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([64]) torch.Size([64])\n",
-      "torch.Size([64]) torch.Size([64])\n",
-      "torch.Size([64]) torch.Size([64])\n"
-     ]
-    }
-   ],
-   "source": [
-    "for idx, (x, y) in enumerate(nyse_dm.train_dataloader()):\n",
-    "    out = nyse_model(x)\n",
-    "    print(y.size(), out.size())\n",
-    "    if idx >= 2:\n",
-    "        break\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eecb6d9c",
-   "metadata": {},
-   "source": [
-    "We follow our previous example for setting up a trainer for a\n",
-    "regression problem, requesting the $R^2$ metric\n",
-    "to be be computed at each epoch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "0d04344c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyse_optimizer = RMSprop(nyse_model.parameters(),\n",
-    "                         lr=0.001)\n",
-    "nyse_module = SimpleModule.regression(nyse_model,\n",
-    "                                      optimizer=nyse_optimizer,\n",
-    "                                      metrics={'r2':R2Score()})\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a10444d",
-   "metadata": {},
-   "source": [
-    "Fitting the model should by now be familiar.\n",
-    "The results on the test data are very similar to the linear AR model. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "485d7bb1",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (mps), used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1789: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
-      "  rank_zero_warn(\n",
-      "\n",
-      "  | Name  | Type      | Params\n",
-      "------------------------------------\n",
-      "0 | model | NYSEModel | 217   \n",
-      "1 | loss  | MSELoss   | 0     \n",
-      "------------------------------------\n",
-      "217       Trainable params\n",
-      "0         Non-trainable params\n",
-      "217       Total params\n",
-      "0.001     Total estimated model params size (MB)\n"
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-     "output_type": "stream",
-     "text": [
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "        test_loss           0.6207807064056396\n",
-      "         test_r2            0.41084879636764526\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'test_loss': 0.6207807064056396, 'test_r2': 0.41084879636764526}]"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nyse_trainer = Trainer(deterministic=True,\n",
-    "                       max_epochs=200,\n",
-    "                       callbacks=[ErrorTracker()])\n",
-    "nyse_trainer.fit(nyse_module,\n",
-    "                 datamodule=nyse_dm)\n",
-    "nyse_trainer.test(nyse_module,\n",
-    "                  datamodule=nyse_dm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbd13ee3",
-   "metadata": {},
-   "source": [
-    "We could also fit a model without the `nn.RNN()` layer by just\n",
-    "using a `nn.Flatten()` layer instead. This would be a nonlinear AR model. If in addition we excluded the \n",
-    "hidden layer, this would be equivalent to our earlier linear AR model.  \n",
-    "\n",
-    "Instead we will fit a nonlinear AR model using the feature set `X_day` that includes the `day_of_week` indicators.\n",
-    "To do so, we\n",
-    "must first create our test and training datasets and a corresponding\n",
-    "data module. This may seem a little burdensome, but is part of the\n",
-    "general pipeline for `torch`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "91d6633a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datasets = []\n",
-    "for mask in [train, ~train]:\n",
-    "    X_day_t = torch.tensor(\n",
-    "                   np.asarray(X_day[mask]).astype(np.float32))\n",
-    "    Y_t = torch.tensor(np.asarray(Y[mask]).astype(np.float32))\n",
-    "    datasets.append(TensorDataset(X_day_t, Y_t))\n",
-    "day_train, day_test = datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5cf941c",
-   "metadata": {},
-   "source": [
-    "Creating a data module follows a familiar pattern."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "87137503",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "day_dm = SimpleDataModule(day_train,\n",
-    "                          day_test,\n",
-    "                          num_workers=min(4, max_num_workers),\n",
-    "                          validation=day_test,\n",
-    "                          batch_size=64)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2575f374",
-   "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "3cf09a55",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NonLinearARModel(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(NonLinearARModel, self).__init__()\n",
-    "        self._forward = nn.Sequential(nn.Flatten(),\n",
-    "                                      nn.Linear(20, 32),\n",
-    "                                      nn.ReLU(),\n",
-    "                                      nn.Dropout(0.5),\n",
-    "                                      nn.Linear(32, 1))\n",
-    "    def forward(self, x):\n",
-    "        return torch.flatten(self._forward(x))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "930576f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nl_model = NonLinearARModel()\n",
-    "nl_optimizer = RMSprop(nl_model.parameters(),\n",
-    "                           lr=0.001)\n",
-    "nl_module = SimpleModule.regression(nl_model,\n",
-    "                                        optimizer=nl_optimizer,\n",
-    "                                        metrics={'r2':R2Score()})\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0e3043c",
-   "metadata": {},
-   "source": [
-    "We continue with the usual training steps, fit the model,\n",
-    "and evaluate the test error. We see the test $R^2$ is a slight improvement over the linear AR model that also includes `day_of_week`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "b9ae8d0e",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (mps), used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "\n",
-      "  | Name  | Type             | Params\n",
-      "-------------------------------------------\n",
-      "0 | model | NonLinearARModel | 705   \n",
-      "1 | loss  | MSELoss          | 0     \n",
-      "-------------------------------------------\n",
-      "705       Trainable params\n",
-      "0         Non-trainable params\n",
-      "705       Total params\n",
-      "0.003     Total estimated model params size (MB)\n"
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-      "IOPub message rate exceeded.\n",
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-      "\n",
-      "Current values:\n",
-      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
-      "ServerApp.rate_limit_window=3.0 (secs)\n",
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-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "IOPub message rate exceeded.\n",
-      "The Jupyter server will temporarily stop sending output\n",
-      "to the client in order to avoid crashing it.\n",
-      "To change this limit, set the config variable\n",
-      "`--ServerApp.iopub_msg_rate_limit`.\n",
-      "\n",
-      "Current values:\n",
-      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
-      "ServerApp.rate_limit_window=3.0 (secs)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "       Test metric             DataLoader 0\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
-      "        test_loss           0.5648325681686401\n",
-      "         test_r2            0.4639463424682617\n",
-      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'test_loss': 0.5648325681686401, 'test_r2': 0.4639463424682617}]"
+       "[{'test_loss': 0.5651810765266418, 'test_r2': 0.4636155962944031}]"
       ]
      },
      "execution_count": 113,
@@ -10063,28 +4862,18 @@
     "nl_trainer.fit(nl_module, datamodule=day_dm)\n",
     "nl_trainer.test(nl_module, datamodule=day_dm) "
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fd356e46",
-   "metadata": {},
-   "source": [
-    "              \n",
-    "\n",
-    " \n"
-   ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python3 (islp_freeze_311)",
    "language": "python",
-   "name": "python3"
+   "name": "islp_freeze_311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -10096,7 +4885,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch11-surv-lab.ipynb b/Ch11-surv-lab.ipynb
index 5ab9f47..1b792ab 100644
--- a/Ch11-surv-lab.ipynb
+++ b/Ch11-surv-lab.ipynb
@@ -5,9 +5,7 @@
    "id": "c7f4eb5a",
    "metadata": {},
    "source": [
-    "\n",
-    "# Chapter 11\n",
-    "\n"
+    "# Chapter 11"
    ]
   },
   {
@@ -34,10 +32,10 @@
    "id": "91ac40fd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:03.064349Z",
-     "iopub.status.busy": "2023-07-26T05:18:03.064029Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.312445Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.312016Z"
+     "iopub.execute_input": "2023-07-26T19:30:26.003002Z",
+     "iopub.status.busy": "2023-07-26T19:30:26.002908Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.205801Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.205421Z"
     }
    },
    "outputs": [],
@@ -46,7 +44,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "from ISLP.models import ModelSpec as MS\n",
-    "from ISLP import load_data\n"
+    "from ISLP import load_data"
    ]
   },
   {
@@ -64,10 +62,10 @@
    "id": "99782418",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.315115Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.314859Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.379768Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.379303Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.207779Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.207612Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.296296Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.295956Z"
     }
    },
    "outputs": [],
@@ -78,7 +76,7 @@
     "from lifelines.statistics import \\\n",
     "     (logrank_test,\n",
     "      multivariate_logrank_test)\n",
-    "from ISLP.survival import sim_time\n"
+    "from ISLP.survival import sim_time"
    ]
   },
   {
@@ -97,10 +95,10 @@
    "id": "3137149a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.382594Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.382386Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.389761Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.389375Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.298114Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.297992Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.304678Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.304388Z"
     }
    },
    "outputs": [
@@ -117,7 +115,7 @@
    ],
    "source": [
     "BrainCancer = load_data('BrainCancer')\n",
-    "BrainCancer.columns\n"
+    "BrainCancer.columns"
    ]
   },
   {
@@ -135,12 +133,11 @@
    "id": "45963c92",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.392289Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.392117Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.396285Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.395840Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:27.306221Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.306076Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.309094Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.308833Z"
+    }
    },
    "outputs": [
     {
@@ -157,7 +154,7 @@
     }
    ],
    "source": [
-    "BrainCancer['sex'].value_counts()\n"
+    "BrainCancer['sex'].value_counts()"
    ]
   },
   {
@@ -166,12 +163,11 @@
    "id": "73be61f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.400201Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.399975Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.403967Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.403581Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:27.310531Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.310431Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.313426Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.313121Z"
+    }
    },
    "outputs": [
     {
@@ -190,7 +186,7 @@
     }
    ],
    "source": [
-    "BrainCancer['diagnosis'].value_counts()\n"
+    "BrainCancer['diagnosis'].value_counts()"
    ]
   },
   {
@@ -199,12 +195,11 @@
    "id": "572f0b9e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.406232Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.406110Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.409773Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.409374Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:27.314866Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.314779Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.317867Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.317610Z"
+    }
    },
    "outputs": [
     {
@@ -221,7 +216,7 @@
     }
    ],
    "source": [
-    "BrainCancer['status'].value_counts()\n"
+    "BrainCancer['status'].value_counts()"
    ]
   },
   {
@@ -255,10 +250,10 @@
    "id": "92c39707",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.411840Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.411663Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.539774Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.539439Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.319267Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.319172Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.501692Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.500343Z"
     }
    },
    "outputs": [
@@ -287,7 +282,7 @@
     "fig, ax = subplots(figsize=(8,8))\n",
     "km = KaplanMeierFitter()\n",
     "km_brain = km.fit(BrainCancer['time'], BrainCancer['status'])\n",
-    "km_brain.plot(label='Kaplan Meier estimate', ax=ax)\n"
+    "km_brain.plot(label='Kaplan Meier estimate', ax=ax)"
    ]
   },
   {
@@ -321,10 +316,10 @@
    "id": "3fc7848c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.542265Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.542002Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.674613Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.674126Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.507068Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.506706Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.653319Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.653011Z"
     }
    },
    "outputs": [
@@ -345,7 +340,7 @@
     "for sex, df in BrainCancer.groupby('sex'):\n",
     "    by_sex[sex] = df\n",
     "    km_sex = km.fit(df['time'], df['status'])\n",
-    "    km_sex.plot(label='Sex=%s' % sex, ax=ax)\n"
+    "    km_sex.plot(label='Sex=%s' % sex, ax=ax)"
    ]
   },
   {
@@ -366,12 +361,11 @@
    "id": "bf30d26f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.679625Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.679410Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.743501Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.743166Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:27.654929Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.654810Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.710525Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.710247Z"
+    }
    },
    "outputs": [
     {
@@ -457,7 +451,7 @@
     "logrank_test(by_sex['Male']['time'],\n",
     "             by_sex['Female']['time'],\n",
     "             by_sex['Male']['status'],\n",
-    "             by_sex['Female']['status'])\n"
+    "             by_sex['Female']['status'])"
    ]
   },
   {
@@ -479,10 +473,10 @@
    "id": "2ab78e07",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.746088Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.745814Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.773972Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.773498Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.712399Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.712248Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.738130Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.737857Z"
     }
    },
    "outputs": [
@@ -548,7 +542,7 @@
     "cox_fit = coxph().fit(model_df,\n",
     "                      'time',\n",
     "                      'status')\n",
-    "cox_fit.summary[['coef', 'se(coef)', 'p']]\n"
+    "cox_fit.summary[['coef', 'se(coef)', 'p']]"
    ]
   },
   {
@@ -572,10 +566,10 @@
    "id": "4716b7b0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.776775Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.776463Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.783362Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.782865Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.739771Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.739667Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.745096Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.744797Z"
     }
    },
    "outputs": [
@@ -654,7 +648,7 @@
     }
    ],
    "source": [
-    "cox_fit.log_likelihood_ratio_test()\n"
+    "cox_fit.log_likelihood_ratio_test()"
    ]
   },
   {
@@ -678,10 +672,10 @@
    "id": "c2767d88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.785790Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.785586Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.824235Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.823739Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.746715Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.746605Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.777796Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.777514Z"
     }
    },
    "outputs": [
@@ -795,7 +789,7 @@
     "fit_all = coxph().fit(all_df,\n",
     "                      'time',\n",
     "                      'status')\n",
-    "fit_all.summary[['coef', 'se(coef)', 'p']]\n"
+    "fit_all.summary[['coef', 'se(coef)', 'p']]"
    ]
   },
   {
@@ -826,10 +820,10 @@
    "id": "ede1d219",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.826852Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.826662Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.831013Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.830464Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.779481Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.779362Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.783153Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.782873Z"
     }
    },
    "outputs": [],
@@ -840,7 +834,7 @@
     "        return pd.Series.mode(series)\n",
     "    else:\n",
     "        return series.mean()\n",
-    "modal_data = cleaned.apply(representative, axis=0)\n"
+    "modal_data = cleaned.apply(representative, axis=0)"
    ]
   },
   {
@@ -859,10 +853,10 @@
    "id": "dc032a71",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.833522Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.833389Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.840549Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.840260Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.784638Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.784539Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.790155Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.789921Z"
     }
    },
    "outputs": [
@@ -969,7 +963,7 @@
     "modal_df = pd.DataFrame(\n",
     "              [modal_data.iloc[0] for _ in range(len(levels))])\n",
     "modal_df['diagnosis'] = levels\n",
-    "modal_df\n"
+    "modal_df"
    ]
   },
   {
@@ -987,10 +981,10 @@
    "id": "e7c1fe43",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.843157Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.843004Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.852876Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.852545Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.791678Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.791583Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.798851Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.798573Z"
     }
    },
    "outputs": [
@@ -1112,7 +1106,7 @@
    "source": [
     "modal_X = all_MS.transform(modal_df)\n",
     "modal_X.index = levels\n",
-    "modal_X\n"
+    "modal_X"
    ]
   },
   {
@@ -1129,12 +1123,11 @@
    "id": "f89fbed7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.855400Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.855197Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.864584Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.864194Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:27.800416Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.800308Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.806596Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.806285Z"
+    }
    },
    "outputs": [
     {
@@ -1271,7 +1264,7 @@
    ],
    "source": [
     "predicted_survival = fit_all.predict_survival_function(modal_X)\n",
-    "predicted_survival\n"
+    "predicted_survival"
    ]
   },
   {
@@ -1290,12 +1283,11 @@
    "id": "8f0329b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.867553Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.867105Z",
-     "iopub.status.idle": "2023-07-26T05:18:04.987503Z",
-     "shell.execute_reply": "2023-07-26T05:18:04.986681Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:27.808158Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.808049Z",
+     "iopub.status.idle": "2023-07-26T19:30:27.914231Z",
+     "shell.execute_reply": "2023-07-26T19:30:27.913906Z"
+    }
    },
    "outputs": [
     {
@@ -1311,7 +1303,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "predicted_survival.plot(ax=ax);\n"
+    "predicted_survival.plot(ax=ax);"
    ]
   },
   {
@@ -1333,10 +1325,10 @@
    "id": "3045bfc0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:04.990450Z",
-     "iopub.status.busy": "2023-07-26T05:18:04.990225Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.181343Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.178676Z"
+     "iopub.execute_input": "2023-07-26T19:30:27.915979Z",
+     "iopub.status.busy": "2023-07-26T19:30:27.915852Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.029175Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.028888Z"
     }
    },
    "outputs": [
@@ -1358,7 +1350,7 @@
     "for result, df in Publication.groupby('posres'):\n",
     "    by_result[result] = df\n",
     "    km_result = km.fit(df['time'], df['status'])\n",
-    "    km_result.plot(label='Result=%d' % result, ax=ax)\n"
+    "    km_result.plot(label='Result=%d' % result, ax=ax)"
    ]
   },
   {
@@ -1378,12 +1370,11 @@
    "id": "d070f716",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.184743Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.184569Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.222538Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.221996Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:28.030858Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.030749Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.059324Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.059043Z"
+    }
    },
    "outputs": [
     {
@@ -1448,7 +1439,7 @@
     "posres_fit = coxph().fit(posres_df,\n",
     "                         'time',\n",
     "                         'status')\n",
-    "posres_fit.summary[['coef', 'se(coef)', 'p']]\n"
+    "posres_fit.summary[['coef', 'se(coef)', 'p']]"
    ]
   },
   {
@@ -1467,10 +1458,10 @@
    "id": "2bbcdd0c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.225196Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.224969Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.270258Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.269721Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.060927Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.060812Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.098525Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.098223Z"
     }
    },
    "outputs": [
@@ -1568,7 +1559,7 @@
     "           intercept=False)\n",
     "coxph().fit(model.fit_transform(Publication),\n",
     "            'time',\n",
-    "            'status').summary[['coef', 'se(coef)', 'p']]\n"
+    "            'status').summary[['coef', 'se(coef)', 'p']]"
    ]
   },
   {
@@ -1602,7 +1593,7 @@
     "`Time` of day (Morning, Afternoon, or Evening). We generate data\n",
     "for these covariates so that all possibilities are equally likely: for\n",
     "instance, morning, afternoon and evening calls are equally likely, and\n",
-    "any number of operators from $5$ to $15$ is equally likely. "
+    "any number of operators from $5$ to $15$ is equally likely."
    ]
   },
   {
@@ -1611,10 +1602,10 @@
    "id": "b8ece43a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.272852Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.272613Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.277670Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.277169Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.100149Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.100033Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.103545Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.103271Z"
     }
    },
    "outputs": [],
@@ -1649,10 +1640,10 @@
    "id": "3e4f766f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.279951Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.279774Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.289519Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.288948Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.105054Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.104974Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.112380Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.112135Z"
     }
    },
    "outputs": [],
@@ -1681,10 +1672,10 @@
    "id": "72f42d14",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.292713Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.292424Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.299084Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.298414Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.113965Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.113885Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.117874Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.117640Z"
     }
    },
    "outputs": [
@@ -1776,7 +1767,7 @@
     }
    ],
    "source": [
-    "X[:5]\n"
+    "X[:5]"
    ]
   },
   {
@@ -1793,17 +1784,17 @@
    "id": "8b921536",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.301800Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.301580Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.340807Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.311843Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.119472Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.119352Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.127116Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.126300Z"
     }
    },
    "outputs": [],
    "source": [
     "true_beta = np.array([0.04, -0.3, 0, 0.2, -0.2])\n",
     "true_linpred = X.dot(true_beta)\n",
-    "hazard = lambda t: 1e-5 * t\n"
+    "hazard = lambda t: 1e-5 * t"
    ]
   },
   {
@@ -1840,16 +1831,15 @@
    "id": "96ce0f99",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.399214Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.386941Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.423513Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.417411Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:28.134404Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.134012Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.139431Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.137672Z"
+    }
    },
    "outputs": [],
    "source": [
-    "cum_hazard = lambda t: 1e-5 * t**2 / 2\n"
+    "cum_hazard = lambda t: 1e-5 * t**2 / 2"
    ]
   },
   {
@@ -1871,17 +1861,17 @@
    "id": "63d78ff9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.450050Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.449814Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.587170Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.586673Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.143620Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.143294Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.332708Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.331511Z"
     }
    },
    "outputs": [],
    "source": [
     "W = np.array([sim_time(l, cum_hazard, rng)\n",
     "              for l in true_linpred])\n",
-    "D['Wait time'] = np.clip(W, 0, 1000)\n"
+    "D['Wait time'] = np.clip(W, 0, 1000)"
    ]
   },
   {
@@ -1900,12 +1890,11 @@
    "id": "fe008dbf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.590252Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.589902Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.596018Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.595604Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:28.337898Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.336191Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.348465Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.347537Z"
+    }
    },
    "outputs": [
     {
@@ -1999,7 +1988,7 @@
     "D['Failed'] = rng.choice([1, 0],\n",
     "                         N,\n",
     "                         p=[0.9, 0.1])\n",
-    "D[:5]\n"
+    "D[:5]"
    ]
   },
   {
@@ -2008,10 +1997,10 @@
    "id": "c3a2bec7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.598122Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.597969Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.601009Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.600579Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.352144Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.351650Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.358415Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.357683Z"
     }
    },
    "outputs": [
@@ -2027,7 +2016,7 @@
     }
    ],
    "source": [
-    "D['Failed'].mean()\n"
+    "D['Failed'].mean()"
    ]
   },
   {
@@ -2044,10 +2033,10 @@
    "id": "2b27af56",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.603318Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.603142Z",
-     "iopub.status.idle": "2023-07-26T05:18:05.827076Z",
-     "shell.execute_reply": "2023-07-26T05:18:05.826151Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.361989Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.361496Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.527528Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.527187Z"
     }
    },
    "outputs": [
@@ -2079,7 +2068,7 @@
     "    by_center[center] = df\n",
     "    km_center = km.fit(df['Wait time'], df['Failed'])\n",
     "    km_center.plot(label='Center=%s' % center, ax=ax)\n",
-    "ax.set_title(\"Probability of Still Being on Hold\")\n"
+    "ax.set_title(\"Probability of Still Being on Hold\")"
    ]
   },
   {
@@ -2096,10 +2085,10 @@
    "id": "9625598d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:05.830554Z",
-     "iopub.status.busy": "2023-07-26T05:18:05.830059Z",
-     "iopub.status.idle": "2023-07-26T05:18:06.008529Z",
-     "shell.execute_reply": "2023-07-26T05:18:06.008079Z"
+     "iopub.execute_input": "2023-07-26T19:30:28.529240Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.529144Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.726030Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.725744Z"
     }
    },
    "outputs": [
@@ -2131,7 +2120,7 @@
     "    by_time[time] = df\n",
     "    km_time = km.fit(df['Wait time'], df['Failed'])\n",
     "    km_time.plot(label='Time=%s' % time, ax=ax)\n",
-    "ax.set_title(\"Probability of Still Being on Hold\")\n"
+    "ax.set_title(\"Probability of Still Being on Hold\")"
    ]
   },
   {
@@ -2152,12 +2141,11 @@
    "id": "75a744ef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:06.012649Z",
-     "iopub.status.busy": "2023-07-26T05:18:06.012037Z",
-     "iopub.status.idle": "2023-07-26T05:18:06.033832Z",
-     "shell.execute_reply": "2023-07-26T05:18:06.033320Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:28.727718Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.727608Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.745292Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.744982Z"
+    }
    },
    "outputs": [
     {
@@ -2242,7 +2230,7 @@
    "source": [
     "multivariate_logrank_test(D['Wait time'],\n",
     "                          D['Center'],\n",
-    "                          D['Failed'])\n"
+    "                          D['Failed'])"
    ]
   },
   {
@@ -2259,12 +2247,11 @@
    "id": "9badb3e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:06.036375Z",
-     "iopub.status.busy": "2023-07-26T05:18:06.036170Z",
-     "iopub.status.idle": "2023-07-26T05:18:06.055850Z",
-     "shell.execute_reply": "2023-07-26T05:18:06.055183Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:28.746932Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.746812Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.764031Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.763730Z"
+    }
    },
    "outputs": [
     {
@@ -2349,7 +2336,7 @@
    "source": [
     "multivariate_logrank_test(D['Wait time'],\n",
     "                          D['Time'],\n",
-    "                          D['Failed'])\n"
+    "                          D['Failed'])"
    ]
   },
   {
@@ -2369,12 +2356,11 @@
    "id": "026e9ff8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:06.058057Z",
-     "iopub.status.busy": "2023-07-26T05:18:06.057906Z",
-     "iopub.status.idle": "2023-07-26T05:18:06.184883Z",
-     "shell.execute_reply": "2023-07-26T05:18:06.184356Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:28.765683Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.765567Z",
+     "iopub.status.idle": "2023-07-26T19:30:28.888155Z",
+     "shell.execute_reply": "2023-07-26T19:30:28.887853Z"
+    }
    },
    "outputs": [
     {
@@ -2457,7 +2443,7 @@
     "        'Center'],\n",
     "        intercept=False).fit_transform(D)\n",
     "F = coxph().fit(X, 'Wait time', 'Failed')\n",
-    "F.log_likelihood_ratio_test()\n"
+    "F.log_likelihood_ratio_test()"
    ]
   },
   {
@@ -2474,12 +2460,11 @@
    "id": "7cab3789",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:06.187428Z",
-     "iopub.status.busy": "2023-07-26T05:18:06.187255Z",
-     "iopub.status.idle": "2023-07-26T05:18:06.315832Z",
-     "shell.execute_reply": "2023-07-26T05:18:06.314374Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:28.889732Z",
+     "iopub.status.busy": "2023-07-26T19:30:28.889626Z",
+     "iopub.status.idle": "2023-07-26T19:30:29.012622Z",
+     "shell.execute_reply": "2023-07-26T19:30:29.012324Z"
+    }
    },
    "outputs": [
     {
@@ -2562,7 +2547,7 @@
     "        'Time'],\n",
     "       intercept=False).fit_transform(D)\n",
     "F = coxph().fit(X, 'Wait time', 'Failed')\n",
-    "F.log_likelihood_ratio_test()\n"
+    "F.log_likelihood_ratio_test()"
    ]
   },
   {
@@ -2582,12 +2567,11 @@
    "id": "5cc4b898",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:06.320288Z",
-     "iopub.status.busy": "2023-07-26T05:18:06.319261Z",
-     "iopub.status.idle": "2023-07-26T05:18:06.585074Z",
-     "shell.execute_reply": "2023-07-26T05:18:06.577564Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:29.014300Z",
+     "iopub.status.busy": "2023-07-26T19:30:29.014186Z",
+     "iopub.status.idle": "2023-07-26T19:30:29.163409Z",
+     "shell.execute_reply": "2023-07-26T19:30:29.160684Z"
+    }
    },
    "outputs": [
     {
@@ -2679,7 +2663,7 @@
     "                  X,\n",
     "                 'Wait time',\n",
     "                 'Failed')\n",
-    "fit_queuing.summary[['coef', 'se(coef)', 'p']]\n"
+    "fit_queuing.summary[['coef', 'se(coef)', 'p']]"
    ]
   },
   {
@@ -2695,16 +2679,15 @@
     " `Operators`, `Center = B`, `Center = C`, \n",
     "`Time = Even.` and `Time = Morn.`   are $0.04$, $-0.3$,\n",
     "$0$,   $0.2$, and $-0.2$, respectively. The coefficient estimates\n",
-    "from the fitted Cox model are fairly accurate.\n",
-    "\n"
+    "from the fitted Cox model are fairly accurate."
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2716,7 +2699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch12-unsup-lab.ipynb b/Ch12-unsup-lab.ipynb
index d76363d..5e45811 100644
--- a/Ch12-unsup-lab.ipynb
+++ b/Ch12-unsup-lab.ipynb
@@ -5,7 +5,6 @@
    "id": "80f16ff6",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 12\n",
     "\n",
     " # Lab: Unsupervised Learning\n",
@@ -22,12 +21,11 @@
    "id": "24559be0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:08.163850Z",
-     "iopub.status.busy": "2023-07-26T05:18:08.163726Z",
-     "iopub.status.idle": "2023-07-26T05:18:09.209261Z",
-     "shell.execute_reply": "2023-07-26T05:18:09.208715Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:30.397560Z",
+     "iopub.status.busy": "2023-07-26T19:30:30.397461Z",
+     "iopub.status.idle": "2023-07-26T19:30:31.427761Z",
+     "shell.execute_reply": "2023-07-26T19:30:31.427247Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -37,7 +35,7 @@
     "from statsmodels.datasets import get_rdataset\n",
     "from sklearn.decomposition import PCA\n",
     "from sklearn.preprocessing import StandardScaler\n",
-    "from ISLP import load_data\n"
+    "from ISLP import load_data"
    ]
   },
   {
@@ -55,10 +53,10 @@
    "id": "06fff57d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:09.212356Z",
-     "iopub.status.busy": "2023-07-26T05:18:09.212070Z",
-     "iopub.status.idle": "2023-07-26T05:18:09.327611Z",
-     "shell.execute_reply": "2023-07-26T05:18:09.327246Z"
+     "iopub.execute_input": "2023-07-26T19:30:31.430129Z",
+     "iopub.status.busy": "2023-07-26T19:30:31.429802Z",
+     "iopub.status.idle": "2023-07-26T19:30:32.657326Z",
+     "shell.execute_reply": "2023-07-26T19:30:32.657026Z"
     }
    },
    "outputs": [],
@@ -69,7 +67,7 @@
     "from scipy.cluster.hierarchy import \\\n",
     "     (dendrogram,\n",
     "      cut_tree)\n",
-    "from ISLP.cluster import compute_linkage\n"
+    "from ISLP.cluster import compute_linkage"
    ]
   },
   {
@@ -92,10 +90,10 @@
    "id": "f425e07e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:09.329909Z",
-     "iopub.status.busy": "2023-07-26T05:18:09.329763Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.559326Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.558692Z"
+     "iopub.execute_input": "2023-07-26T19:30:32.659129Z",
+     "iopub.status.busy": "2023-07-26T19:30:32.659013Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.336349Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.336069Z"
     }
    },
    "outputs": [
@@ -542,7 +540,7 @@
    ],
    "source": [
     "USArrests = get_rdataset('USArrests').data\n",
-    "USArrests\n"
+    "USArrests"
    ]
   },
   {
@@ -559,10 +557,10 @@
    "id": "b127d014",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.561457Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.561326Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.564289Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.563966Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.337956Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.337851Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.340085Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.339853Z"
     }
    },
    "outputs": [
@@ -578,7 +576,7 @@
     }
    ],
    "source": [
-    "USArrests.columns\n"
+    "USArrests.columns"
    ]
   },
   {
@@ -595,12 +593,11 @@
    "id": "c7343f72",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.566649Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.566503Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.570319Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.569890Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:33.341585Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.341485Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.344182Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.343905Z"
+    }
    },
    "outputs": [
     {
@@ -619,7 +616,7 @@
     }
    ],
    "source": [
-    "USArrests.mean()\n"
+    "USArrests.mean()"
    ]
   },
   {
@@ -638,10 +635,10 @@
    "id": "34501140",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.572610Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.572485Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.575894Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.575543Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.345660Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.345555Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.348085Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.347836Z"
     }
    },
    "outputs": [
@@ -661,7 +658,7 @@
     }
    ],
    "source": [
-    "USArrests.var()\n"
+    "USArrests.var()"
    ]
   },
   {
@@ -683,7 +680,7 @@
     "This scaling can be done via the `StandardScaler()` transform imported above. We first `fit` the\n",
     "scaler, which computes the necessary means and standard\n",
     "deviations and then apply it to our data using the\n",
-    "`transform` method. As before, we combine these steps using the `fit_transform()` method.\n"
+    "`transform` method. As before, we combine these steps using the `fit_transform()` method."
    ]
   },
   {
@@ -692,18 +689,17 @@
    "id": "daf119e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.578199Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.578025Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.581928Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.581446Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.349511Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.349412Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.352394Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.352150Z"
+    }
    },
    "outputs": [],
    "source": [
     "scaler = StandardScaler(with_std=True,\n",
     "                        with_mean=True)\n",
-    "USArrests_scaled = scaler.fit_transform(USArrests)\n"
+    "USArrests_scaled = scaler.fit_transform(USArrests)"
    ]
   },
   {
@@ -722,16 +718,15 @@
    "id": "a0eda7c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.584181Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.584070Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.586440Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.586052Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.353785Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.353683Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.355373Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.355099Z"
+    }
    },
    "outputs": [],
    "source": [
-    "pcaUS = PCA()\n"
+    "pcaUS = PCA()"
    ]
   },
   {
@@ -751,17 +746,17 @@
    "id": "1430fb3c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.588957Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.588809Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.593315Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.592971Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.356817Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.356721Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.360978Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.360719Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
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+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "PCA()"
@@ -773,7 +768,7 @@
     }
    ],
    "source": [
-    "pcaUS.fit(USArrests_scaled)\n"
+    "pcaUS.fit(USArrests_scaled)"
    ]
   },
   {
@@ -792,10 +787,10 @@
    "id": "6131d8d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.595542Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.595388Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.598527Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.598057Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.362368Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.362292Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.364473Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.364225Z"
     }
    },
    "outputs": [
@@ -811,7 +806,7 @@
     }
    ],
    "source": [
-    "pcaUS.mean_\n"
+    "pcaUS.mean_"
    ]
   },
   {
@@ -829,16 +824,15 @@
    "id": "08246aad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.600656Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.600536Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.603024Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.602498Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.365873Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.365798Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.367607Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.367362Z"
+    }
    },
    "outputs": [],
    "source": [
-    "scores = pcaUS.transform(USArrests_scaled)\n"
+    "scores = pcaUS.transform(USArrests_scaled)"
    ]
   },
   {
@@ -849,7 +843,7 @@
     "We will plot these scores a bit further down.\n",
     "The `components_` attribute provides the principal component loadings:\n",
     "each row of `pcaUS.components_` contains the corresponding\n",
-    "principal component loading vector.\n"
+    "principal component loading vector."
    ]
   },
   {
@@ -858,10 +852,10 @@
    "id": "b682b632",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.605166Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.605000Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.607923Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.607498Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.368982Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.368908Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.371017Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.370774Z"
     }
    },
    "outputs": [
@@ -880,7 +874,7 @@
     }
    ],
    "source": [
-    "pcaUS.components_ \n"
+    "pcaUS.components_ "
    ]
   },
   {
@@ -901,12 +895,11 @@
    "id": "c165e990",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.610624Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.610442Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.736284Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.735735Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.372421Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.372348Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.549941Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.549061Z"
+    }
    },
    "outputs": [
     {
@@ -930,7 +923,7 @@
     "    ax.arrow(0, 0, pcaUS.components_[i,k], pcaUS.components_[j,k])\n",
     "    ax.text(pcaUS.components_[i,k],\n",
     "            pcaUS.components_[j,k],\n",
-    "            USArrests.columns[k])\n"
+    "            USArrests.columns[k])"
    ]
   },
   {
@@ -951,10 +944,10 @@
    "id": "848c9f35",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.738926Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.738771Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.848441Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.847972Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.555869Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.555474Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.721362Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.721044Z"
     }
    },
    "outputs": [
@@ -981,7 +974,7 @@
     "    ax.arrow(0, 0, s_*pcaUS.components_[i,k], s_*pcaUS.components_[j,k])\n",
     "    ax.text(s_*pcaUS.components_[i,k],\n",
     "            s_*pcaUS.components_[j,k],\n",
-    "            USArrests.columns[k])\n"
+    "            USArrests.columns[k])"
    ]
   },
   {
@@ -998,12 +991,11 @@
    "id": "34fdfe21",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.852220Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.851752Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.859190Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.857389Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:33.723071Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.722963Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.725587Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.725242Z"
+    }
    },
    "outputs": [
     {
@@ -1036,12 +1028,11 @@
    "id": "31b43c57",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.861366Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.861239Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.864540Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.863998Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.727152Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.727025Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.729277Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.728999Z"
+    }
    },
    "outputs": [
     {
@@ -1056,7 +1047,7 @@
     }
    ],
    "source": [
-    "pcaUS.explained_variance_\n"
+    "pcaUS.explained_variance_"
    ]
   },
   {
@@ -1074,12 +1065,11 @@
    "id": "68e47d3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.867030Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.866863Z",
-     "iopub.status.idle": "2023-07-26T05:18:10.869949Z",
-     "shell.execute_reply": "2023-07-26T05:18:10.869455Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.730782Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.730677Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.732945Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.732671Z"
+    }
    },
    "outputs": [
     {
@@ -1094,7 +1084,7 @@
     }
    ],
    "source": [
-    "pcaUS.explained_variance_ratio_\n"
+    "pcaUS.explained_variance_ratio_"
    ]
   },
   {
@@ -1115,12 +1105,11 @@
    "id": "e87fe198",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:10.872333Z",
-     "iopub.status.busy": "2023-07-26T05:18:10.872213Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.030288Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.029440Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.734468Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.734368Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.863414Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.863078Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -1134,7 +1123,7 @@
     "ax.set_xlabel('Principal Component');\n",
     "ax.set_ylabel('Proportion of Variance Explained')\n",
     "ax.set_ylim([0,1])\n",
-    "ax.set_xticks(ticks)\n"
+    "ax.set_xticks(ticks)"
    ]
   },
   {
@@ -1151,12 +1140,11 @@
    "id": "409fb0c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.033250Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.033097Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.153804Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.153245Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.865434Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.865319Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.969465Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.969166Z"
+    }
    },
    "outputs": [
     {
@@ -1180,7 +1168,7 @@
     "ax.set_ylabel('Cumulative Proportion of Variance Explained')\n",
     "ax.set_ylim([0, 1])\n",
     "ax.set_xticks(ticks)\n",
-    "fig\n"
+    "fig"
    ]
   },
   {
@@ -1199,12 +1187,11 @@
    "id": "e563e41b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.157597Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.157216Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.162432Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.162026Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.971250Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.971143Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.973649Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.973332Z"
+    }
    },
    "outputs": [
     {
@@ -1220,7 +1207,7 @@
    ],
    "source": [
     "a = np.array([1,2,8,-3])\n",
-    "np.cumsum(a)\n"
+    "np.cumsum(a)"
    ]
   },
   {
@@ -1239,7 +1226,7 @@
     "components of the data.  We use our scaled\n",
     "and centered `USArrests` data as $\\bf X$ below. The *singular value decomposition* \n",
     "(SVD)  is a general algorithm for solving\n",
-    "(12.6). "
+    "(12.6)."
    ]
   },
   {
@@ -1248,12 +1235,11 @@
    "id": "f83ad0bc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.164364Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.164225Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.167031Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.166724Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.975247Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.975135Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.977744Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.977458Z"
+    }
    },
    "outputs": [
     {
@@ -1270,7 +1256,7 @@
    "source": [
     "X = USArrests_scaled\n",
     "U, D, V = np.linalg.svd(X, full_matrices=False)\n",
-    "U.shape, D.shape, V.shape\n"
+    "U.shape, D.shape, V.shape"
    ]
   },
   {
@@ -1289,10 +1275,10 @@
    "id": "cb9bdc46",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.169269Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.169096Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.172167Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.171624Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.979234Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.979142Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.981348Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.981099Z"
     }
    },
    "outputs": [
@@ -1300,8 +1286,8 @@
      "data": {
       "text/plain": [
        "array([[-0.53589947, -0.58318363, -0.27819087, -0.54343209],\n",
-       "       [ 0.41818087,  0.1879856 , -0.87280619, -0.16731864],\n",
-       "       [-0.34123273, -0.26814843, -0.37801579,  0.81777791],\n",
+       "       [-0.41818087, -0.1879856 ,  0.87280619,  0.16731864],\n",
+       "       [ 0.34123273,  0.26814843,  0.37801579, -0.81777791],\n",
        "       [ 0.6492278 , -0.74340748,  0.13387773,  0.08902432]])"
       ]
      },
@@ -1311,7 +1297,7 @@
     }
    ],
    "source": [
-    "V\n"
+    "V"
    ]
   },
   {
@@ -1320,12 +1306,11 @@
    "id": "f23c101e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.174404Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.174265Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.177096Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.176712Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.982741Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.982632Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.984773Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.984538Z"
+    }
    },
    "outputs": [
     {
@@ -1343,7 +1328,7 @@
     }
    ],
    "source": [
-    "pcaUS.components_\n"
+    "pcaUS.components_"
    ]
   },
   {
@@ -1360,19 +1345,19 @@
    "id": "4cc49622",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.179219Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.179054Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.182070Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.181699Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.986227Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.986126Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.988324Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.988065Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[-0.98556588,  1.13339238, -0.44426879,  0.15626714],\n",
-       "       [-1.95013775,  1.07321326,  2.04000333, -0.43858344],\n",
-       "       [-1.76316354, -0.74595678,  0.05478082, -0.83465292]])"
+       "array([[-0.98556588, -1.13339238,  0.44426879,  0.15626714],\n",
+       "       [-1.95013775, -1.07321326, -2.04000333, -0.43858344],\n",
+       "       [-1.76316354,  0.74595678, -0.05478082, -0.83465292]])"
       ]
      },
      "execution_count": 24,
@@ -1381,7 +1366,7 @@
     }
    ],
    "source": [
-    "(U * D[None,:])[:3]\n"
+    "(U * D[None,:])[:3]"
    ]
   },
   {
@@ -1390,12 +1375,11 @@
    "id": "c96c9fe1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.184051Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.183934Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.186945Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.186473Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.989702Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.989597Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.991706Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.991447Z"
+    }
    },
    "outputs": [
     {
@@ -1412,7 +1396,7 @@
     }
    ],
    "source": [
-    "scores[:3]\n"
+    "scores[:3]"
    ]
   },
   {
@@ -1435,10 +1419,10 @@
    "id": "574409d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.189376Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.189199Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.192254Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.191873Z"
+     "iopub.execute_input": "2023-07-26T19:30:33.993218Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.993112Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.995440Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.995155Z"
     }
    },
    "outputs": [],
@@ -1452,7 +1436,7 @@
     "                         n_omit,\n",
     "                         replace=True)\n",
     "Xna = X.copy()\n",
-    "Xna[r_idx, c_idx] = np.nan\n"
+    "Xna[r_idx, c_idx] = np.nan"
    ]
   },
   {
@@ -1475,19 +1459,18 @@
    "id": "89f190ae",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.194569Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.194458Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.197204Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.196762Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:33.996982Z",
+     "iopub.status.busy": "2023-07-26T19:30:33.996879Z",
+     "iopub.status.idle": "2023-07-26T19:30:33.998962Z",
+     "shell.execute_reply": "2023-07-26T19:30:33.998692Z"
+    }
    },
    "outputs": [],
    "source": [
     "def low_rank(X, M=1):\n",
     "    U, D, V = np.linalg.svd(X)\n",
     "    L = U[:,:M] * D[None,:M]\n",
-    "    return L.dot(V[:M])\n"
+    "    return L.dot(V[:M])"
    ]
   },
   {
@@ -1508,17 +1491,17 @@
    "id": "322f339c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.199355Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.199208Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.201784Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.201348Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.000373Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.000273Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.002201Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.001944Z"
     }
    },
    "outputs": [],
    "source": [
     "Xhat = Xna.copy()\n",
     "Xbar = np.nanmean(Xhat, axis=0)\n",
-    "Xhat[r_idx, c_idx] = Xbar[c_idx]\n"
+    "Xhat[r_idx, c_idx] = Xbar[c_idx]"
    ]
   },
   {
@@ -1536,12 +1519,11 @@
    "id": "7e106d1a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.204020Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.203890Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.206286Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.205915Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:34.003661Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.003556Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.005555Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.005295Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -1550,7 +1532,7 @@
     "count = 0\n",
     "ismiss = np.isnan(Xna)\n",
     "mssold = np.mean(Xhat[~ismiss]**2)\n",
-    "mss0 = np.mean(Xna[~ismiss]**2)\n"
+    "mss0 = np.mean(Xna[~ismiss]**2)"
    ]
   },
   {
@@ -1576,10 +1558,10 @@
    "id": "7cb05ce4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.208518Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.208345Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.212476Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.211847Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.007097Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.006994Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.010195Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.009940Z"
     }
    },
    "outputs": [
@@ -1611,7 +1593,7 @@
     "    rel_err = (mssold - mss) / mss0\n",
     "    mssold = mss\n",
     "    print(\"Iteration: {0}, MSS:{1:.3f}, Rel.Err {2:.2e}\"\n",
-    "          .format(count, mss, rel_err))\n"
+    "          .format(count, mss, rel_err))"
    ]
   },
   {
@@ -1631,18 +1613,17 @@
    "id": "6f245188",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.214882Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.214746Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.217935Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.217596Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:34.011740Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.011655Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.014111Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.013837Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.711356743429736"
+       "0.7113567434297362"
       ]
      },
      "execution_count": 31,
@@ -1651,7 +1632,7 @@
     }
    ],
    "source": [
-    "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]\n"
+    "np.corrcoef(Xapp[ismiss], X[ismiss])[0,1]"
    ]
   },
   {
@@ -1689,19 +1670,18 @@
    "id": "345fb41e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.220117Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.219983Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.222355Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.221948Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:34.015675Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.015593Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.017675Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.017398Z"
+    }
    },
    "outputs": [],
    "source": [
     "np.random.seed(0);\n",
     "X = np.random.standard_normal((50,2));\n",
     "X[:25,0] += 3;\n",
-    "X[:25,1] -= 4;\n"
+    "X[:25,1] -= 4;"
    ]
   },
   {
@@ -1718,18 +1698,17 @@
    "id": "3a8c21a2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.224388Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.224258Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.262343Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.261799Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:34.019202Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.019129Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.332124Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.331585Z"
+    }
    },
    "outputs": [],
    "source": [
     "kmeans = KMeans(n_clusters=2,\n",
     "                random_state=2,\n",
-    "                n_init=20).fit(X)\n"
+    "                n_init=20).fit(X)"
    ]
   },
   {
@@ -1746,20 +1725,19 @@
    "id": "e3e35b5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.265599Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.265438Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.268983Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.268283Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:34.334497Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.334360Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.337491Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.337143Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
-       "       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0], dtype=int32)"
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
+       "       0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1], dtype=int32)"
       ]
      },
      "execution_count": 34,
@@ -1768,7 +1746,7 @@
     }
    ],
    "source": [
-    "kmeans.labels_\n"
+    "kmeans.labels_"
    ]
   },
   {
@@ -1788,16 +1766,16 @@
    "id": "d928650a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.271491Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.271353Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.409914Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.409365Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.339433Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.339327Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.448148Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.447830Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1809,7 +1787,7 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=(8,8))\n",
     "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n",
-    "ax.set_title(\"K-Means Clustering Results with K=2\");\n"
+    "ax.set_title(\"K-Means Clustering Results with K=2\");"
    ]
   },
   {
@@ -1835,17 +1813,16 @@
    "id": "92e5175c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.412487Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.412250Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.548124Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.547642Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:34.450210Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.450084Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.561806Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.561471Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1860,7 +1837,7 @@
     "                n_init=20).fit(X)\n",
     "fig, ax = plt.subplots(figsize=(8,8))\n",
     "ax.scatter(X[:,0], X[:,1], c=kmeans.labels_)\n",
-    "ax.set_title(\"K-Means Clustering Results with K=3\");\n"
+    "ax.set_title(\"K-Means Clustering Results with K=3\");"
    ]
   },
   {
@@ -1884,18 +1861,17 @@
    "id": "4911ecc7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.551298Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.551122Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.570392Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.569843Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:34.563762Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.563654Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.576434Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.576080Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(78.06117325882116, 75.0350825910044)"
+       "(76.85131986999252, 75.06261242745384)"
       ]
      },
      "execution_count": 37,
@@ -1910,7 +1886,7 @@
     "kmeans20 = KMeans(n_clusters=3,\n",
     "                  random_state=3,\n",
     "                  n_init=20).fit(X);\n",
-    "kmeans1.inertia_, kmeans20.inertia_\n"
+    "kmeans1.inertia_, kmeans20.inertia_"
    ]
   },
   {
@@ -1957,17 +1933,17 @@
    "id": "1b42a700",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.573065Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.572885Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.577747Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.577317Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.578301Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.578191Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.583704Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.583419Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(distance_threshold=0, linkage=&#x27;complete&#x27;,\n",
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(distance_threshold=0, linkage=&#x27;complete&#x27;,\n",
        "                        n_clusters=None)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AgglomerativeClustering</label><div class=\"sk-toggleable__content\"><pre>AgglomerativeClustering(distance_threshold=0, linkage=&#x27;complete&#x27;,\n",
        "                        n_clusters=None)</pre></div></div></div></div></div>"
       ],
@@ -1986,7 +1962,7 @@
     "hc_comp = HClust(distance_threshold=0,\n",
     "                 n_clusters=None,\n",
     "                 linkage='complete')\n",
-    "hc_comp.fit(X)\n"
+    "hc_comp.fit(X)"
    ]
   },
   {
@@ -2004,10 +1980,10 @@
    "id": "50ef7eea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.580226Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.580003Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.583835Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.583381Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.585516Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.585409Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.588390Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.588091Z"
     }
    },
    "outputs": [],
@@ -2019,7 +1995,7 @@
     "hc_sing = HClust(distance_threshold=0,\n",
     "                 n_clusters=None,\n",
     "                 linkage='single');\n",
-    "hc_sing.fit(X);\n"
+    "hc_sing.fit(X);"
    ]
   },
   {
@@ -2037,17 +2013,17 @@
    "id": "bf7a2408",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.586413Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.586258Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.591990Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.591553Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.590120Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.590008Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.594741Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.594449Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(distance_threshold=0, linkage=&#x27;single&#x27;,\n",
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AgglomerativeClustering(distance_threshold=0, linkage=&#x27;single&#x27;,\n",
        "                        metric=&#x27;precomputed&#x27;, n_clusters=None)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AgglomerativeClustering</label><div class=\"sk-toggleable__content\"><pre>AgglomerativeClustering(distance_threshold=0, linkage=&#x27;single&#x27;,\n",
        "                        metric=&#x27;precomputed&#x27;, n_clusters=None)</pre></div></div></div></div></div>"
       ],
@@ -2070,7 +2046,7 @@
     "                     n_clusters=None,\n",
     "                     metric='precomputed',\n",
     "                     linkage='single')\n",
-    "hc_sing_pre.fit(D)\n"
+    "hc_sing_pre.fit(D)"
    ]
   },
   {
@@ -2097,10 +2073,10 @@
    "id": "a118c0ab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.595666Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.595400Z",
-     "iopub.status.idle": "2023-07-26T05:18:11.876074Z",
-     "shell.execute_reply": "2023-07-26T05:18:11.875555Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.596509Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.596402Z",
+     "iopub.status.idle": "2023-07-26T19:30:34.821096Z",
+     "shell.execute_reply": "2023-07-26T19:30:34.820763Z"
     }
    },
    "outputs": [
@@ -2122,7 +2098,7 @@
     "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
     "dendrogram(linkage_comp,\n",
     "           ax=ax,\n",
-    "           **cargs);\n"
+    "           **cargs);"
    ]
   },
   {
@@ -2142,10 +2118,10 @@
    "id": "b1ff41c0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:11.878711Z",
-     "iopub.status.busy": "2023-07-26T05:18:11.878497Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.156685Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.156217Z"
+     "iopub.execute_input": "2023-07-26T19:30:34.822795Z",
+     "iopub.status.busy": "2023-07-26T19:30:34.822686Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.046746Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.046456Z"
     }
    },
    "outputs": [
@@ -2165,7 +2141,7 @@
     "dendrogram(linkage_comp,\n",
     "           ax=ax,\n",
     "           color_threshold=4,\n",
-    "           above_threshold_color='black');\n"
+    "           above_threshold_color='black');"
    ]
   },
   {
@@ -2184,10 +2160,10 @@
    "id": "c2752a96",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.159239Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.159059Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.163542Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.163127Z"
+     "iopub.execute_input": "2023-07-26T19:30:35.048471Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.048346Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.051861Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.051581Z"
     }
    },
    "outputs": [
@@ -2205,7 +2181,7 @@
     }
    ],
    "source": [
-    "cut_tree(linkage_comp, n_clusters=4).T\n"
+    "cut_tree(linkage_comp, n_clusters=4).T"
    ]
   },
   {
@@ -2226,10 +2202,10 @@
    "id": "1407f7a4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.165971Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.165813Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.170809Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.170332Z"
+     "iopub.execute_input": "2023-07-26T19:30:35.053497Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.053410Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.056899Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.056632Z"
     }
    },
    "outputs": [
@@ -2294,7 +2270,7 @@
     }
    ],
    "source": [
-    "cut_tree(linkage_comp, height=5)\n"
+    "cut_tree(linkage_comp, height=5)"
    ]
   },
   {
@@ -2312,10 +2288,10 @@
    "id": "2d74f224",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.173359Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.173198Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.457460Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.457026Z"
+     "iopub.execute_input": "2023-07-26T19:30:35.058528Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.058413Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.293973Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.293682Z"
     }
    },
    "outputs": [
@@ -2339,7 +2315,7 @@
     "linkage_comp_scale = compute_linkage(hc_comp_scale)\n",
     "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
     "dendrogram(linkage_comp_scale, ax=ax, **cargs)\n",
-    "ax.set_title(\"Hierarchical Clustering with Scaled Features\");\n"
+    "ax.set_title(\"Hierarchical Clustering with Scaled Features\");"
    ]
   },
   {
@@ -2367,12 +2343,11 @@
    "id": "b7f7da12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.461207Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.460949Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.680696Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.680192Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:35.295662Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.295546Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.475780Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.475471Z"
+    }
    },
    "outputs": [
     {
@@ -2397,7 +2372,7 @@
     "linkage_cor = compute_linkage(hc_cor)\n",
     "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
     "dendrogram(linkage_cor, ax=ax, **cargs)\n",
-    "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");\n"
+    "ax.set_title(\"Complete Linkage with Correlation-Based Dissimilarity\");"
    ]
   },
   {
@@ -2419,17 +2394,17 @@
    "id": "b94424fc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.683099Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.682918Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.688821Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.688474Z"
+     "iopub.execute_input": "2023-07-26T19:30:35.477384Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.477277Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.483374Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.483097Z"
     }
    },
    "outputs": [],
    "source": [
     "NCI60 = load_data('NCI60')\n",
     "nci_labs = NCI60['labels']\n",
-    "nci_data = NCI60['data']\n"
+    "nci_data = NCI60['data']"
    ]
   },
   {
@@ -2452,12 +2427,11 @@
    "id": "cea54566",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.691075Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.690934Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.693705Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.693319Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:35.485015Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.484926Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.487365Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.487014Z"
+    }
    },
    "outputs": [
     {
@@ -2472,7 +2446,7 @@
     }
    ],
    "source": [
-    "nci_data.shape\n"
+    "nci_data.shape"
    ]
   },
   {
@@ -2480,7 +2454,7 @@
    "id": "6ebf27f1",
    "metadata": {},
    "source": [
-    "We begin by examining the cancer types for the cell lines.\n"
+    "We begin by examining the cancer types for the cell lines."
    ]
   },
   {
@@ -2489,12 +2463,11 @@
    "id": "4dac41bb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.695954Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.695815Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.700521Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.700074Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:35.488952Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.488850Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.492808Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.492520Z"
+    }
    },
    "outputs": [
     {
@@ -2524,7 +2497,7 @@
     }
    ],
    "source": [
-    "nci_labs.value_counts()\n"
+    "nci_labs.value_counts()"
    ]
   },
   {
@@ -2545,10 +2518,10 @@
    "id": "d8ebadd6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.702785Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.702646Z",
-     "iopub.status.idle": "2023-07-26T05:18:12.866311Z",
-     "shell.execute_reply": "2023-07-26T05:18:12.856469Z"
+     "iopub.execute_input": "2023-07-26T19:30:35.494320Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.494231Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.693502Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.692601Z"
     }
    },
    "outputs": [],
@@ -2556,7 +2529,7 @@
     "scaler = StandardScaler()\n",
     "nci_scaled = scaler.fit_transform(nci_data)\n",
     "nci_pca = PCA()\n",
-    "nci_scores = nci_pca.fit_transform(nci_scaled)\n"
+    "nci_scores = nci_pca.fit_transform(nci_scaled)"
    ]
   },
   {
@@ -2568,7 +2541,7 @@
     "to visualize the data. The observations (cell lines) corresponding to\n",
     "a given cancer type will be plotted in the same color, so that we can\n",
     "see to what extent the observations within a cancer type are similar\n",
-    "to each other. "
+    "to each other."
    ]
   },
   {
@@ -2577,12 +2550,11 @@
    "id": "63b5efe3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:12.882679Z",
-     "iopub.status.busy": "2023-07-26T05:18:12.878318Z",
-     "iopub.status.idle": "2023-07-26T05:18:13.099177Z",
-     "shell.execute_reply": "2023-07-26T05:18:13.098712Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:35.701630Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.701157Z",
+     "iopub.status.idle": "2023-07-26T19:30:35.917928Z",
+     "shell.execute_reply": "2023-07-26T19:30:35.917230Z"
+    }
    },
    "outputs": [
     {
@@ -2614,7 +2586,7 @@
     "           c=nci_groups,\n",
     "           marker='o',\n",
     "           s=50)\n",
-    "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');\n"
+    "ax.set_xlabel('PC1'); ax.set_ylabel('PC3');"
    ]
   },
   {
@@ -2625,10 +2597,7 @@
     "On the whole, cell lines corresponding to a single cancer type do tend to\n",
     "have similar values on the first few principal component score\n",
     "vectors. This indicates that cell lines from the same cancer type tend\n",
-    "to have pretty similar gene expression levels.\n",
-    "\n",
-    "\n",
-    "    "
+    "to have pretty similar gene expression levels."
    ]
   },
   {
@@ -2647,12 +2616,11 @@
    "id": "e20c3cc1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:13.101683Z",
-     "iopub.status.busy": "2023-07-26T05:18:13.101534Z",
-     "iopub.status.idle": "2023-07-26T05:18:13.327522Z",
-     "shell.execute_reply": "2023-07-26T05:18:13.327041Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:35.923780Z",
+     "iopub.status.busy": "2023-07-26T19:30:35.923226Z",
+     "iopub.status.idle": "2023-07-26T19:30:36.085072Z",
+     "shell.execute_reply": "2023-07-26T19:30:36.084763Z"
+    }
    },
    "outputs": [
     {
@@ -2680,7 +2648,7 @@
     "        nci_pca.explained_variance_ratio_.cumsum(),\n",
     "        marker='o');\n",
     "ax.set_xlabel('Principal Component')\n",
-    "ax.set_ylabel('Cumulative PVE');\n"
+    "ax.set_ylabel('Cumulative PVE');"
    ]
   },
   {
@@ -2720,10 +2688,10 @@
    "id": "622de805",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:13.330190Z",
-     "iopub.status.busy": "2023-07-26T05:18:13.330013Z",
-     "iopub.status.idle": "2023-07-26T05:18:13.332964Z",
-     "shell.execute_reply": "2023-07-26T05:18:13.332533Z"
+     "iopub.execute_input": "2023-07-26T19:30:36.086757Z",
+     "iopub.status.busy": "2023-07-26T19:30:36.086641Z",
+     "iopub.status.idle": "2023-07-26T19:30:36.088991Z",
+     "shell.execute_reply": "2023-07-26T19:30:36.088712Z"
     }
    },
    "outputs": [],
@@ -2741,7 +2709,7 @@
     "               leaf_font_size=10,\n",
     "               **cargs)\n",
     "    ax.set_title('%s Linkage' % linkage)\n",
-    "    return hc\n"
+    "    return hc"
    ]
   },
   {
@@ -2758,12 +2726,11 @@
    "id": "54d40449",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:13.336193Z",
-     "iopub.status.busy": "2023-07-26T05:18:13.335972Z",
-     "iopub.status.idle": "2023-07-26T05:18:14.886877Z",
-     "shell.execute_reply": "2023-07-26T05:18:14.886190Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:36.090487Z",
+     "iopub.status.busy": "2023-07-26T19:30:36.090390Z",
+     "iopub.status.idle": "2023-07-26T19:30:37.387883Z",
+     "shell.execute_reply": "2023-07-26T19:30:37.387596Z"
+    }
    },
    "outputs": [
     {
@@ -2781,7 +2748,7 @@
     "fig, axes = plt.subplots(3, 1, figsize=(15,30))      \n",
     "ax = axes[0]; hc_comp = plot_nci('Complete', ax)\n",
     "ax = axes[1]; hc_avg = plot_nci('Average', ax)\n",
-    "ax = axes[2]; hc_sing = plot_nci('Single', ax)\n"
+    "ax = axes[2]; hc_sing = plot_nci('Single', ax)"
    ]
   },
   {
@@ -2811,12 +2778,11 @@
    "id": "dc80afc8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:14.891531Z",
-     "iopub.status.busy": "2023-07-26T05:18:14.890910Z",
-     "iopub.status.idle": "2023-07-26T05:18:14.903469Z",
-     "shell.execute_reply": "2023-07-26T05:18:14.903077Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:37.389840Z",
+     "iopub.status.busy": "2023-07-26T19:30:37.389731Z",
+     "iopub.status.idle": "2023-07-26T19:30:37.399764Z",
+     "shell.execute_reply": "2023-07-26T19:30:37.399508Z"
+    }
    },
    "outputs": [
     {
@@ -2984,7 +2950,7 @@
     "linkage_comp = compute_linkage(hc_comp)\n",
     "comp_cut = cut_tree(linkage_comp, n_clusters=4).reshape(-1)\n",
     "pd.crosstab(nci_labs['label'],\n",
-    "            pd.Series(comp_cut.reshape(-1), name='Complete'))\n"
+    "            pd.Series(comp_cut.reshape(-1), name='Complete'))"
    ]
   },
   {
@@ -3005,10 +2971,10 @@
    "id": "40ff59f9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:14.905992Z",
-     "iopub.status.busy": "2023-07-26T05:18:14.905760Z",
-     "iopub.status.idle": "2023-07-26T05:18:15.399751Z",
-     "shell.execute_reply": "2023-07-26T05:18:15.399237Z"
+     "iopub.execute_input": "2023-07-26T19:30:37.401281Z",
+     "iopub.status.busy": "2023-07-26T19:30:37.401178Z",
+     "iopub.status.idle": "2023-07-26T19:30:37.802871Z",
+     "shell.execute_reply": "2023-07-26T19:30:37.802572Z"
     }
    },
    "outputs": [
@@ -3026,7 +2992,7 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(10,10))\n",
     "plot_nci('Complete', ax, cut=140)\n",
-    "ax.axhline(140, c='r', linewidth=4);\n"
+    "ax.axhline(140, c='r', linewidth=4);"
    ]
   },
   {
@@ -3054,10 +3020,10 @@
    "id": "1587e83b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:15.401922Z",
-     "iopub.status.busy": "2023-07-26T05:18:15.401768Z",
-     "iopub.status.idle": "2023-07-26T05:18:17.931480Z",
-     "shell.execute_reply": "2023-07-26T05:18:17.924183Z"
+     "iopub.execute_input": "2023-07-26T19:30:37.804622Z",
+     "iopub.status.busy": "2023-07-26T19:30:37.804518Z",
+     "iopub.status.idle": "2023-07-26T19:30:38.480316Z",
+     "shell.execute_reply": "2023-07-26T19:30:38.409430Z"
     }
    },
    "outputs": [
@@ -3098,30 +3064,30 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>28</td>\n",
-       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>10</td>\n",
        "      <td>9</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>7</td>\n",
        "      <td>0</td>\n",
+       "      <td>7</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0</td>\n",
-       "      <td>9</td>\n",
        "      <td>0</td>\n",
+       "      <td>9</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -3129,12 +3095,12 @@
        "</div>"
       ],
       "text/plain": [
-       "K-means   0  1  2  3\n",
-       "HClust              \n",
-       "0        28  3  9  0\n",
-       "1         7  0  0  0\n",
-       "2         0  0  0  8\n",
-       "3         0  9  0  0"
+       "K-means  0   1   2  3\n",
+       "HClust               \n",
+       "0        1  20  10  9\n",
+       "1        0   7   0  0\n",
+       "2        8   0   0  0\n",
+       "3        0   0   9  0"
       ]
      },
      "execution_count": 57,
@@ -3147,7 +3113,7 @@
     "                    random_state=0,\n",
     "                    n_init=20).fit(nci_scaled)\n",
     "pd.crosstab(pd.Series(comp_cut, name='HClust'),\n",
-    "            pd.Series(nci_kmeans.labels_, name='K-means'))\n"
+    "            pd.Series(nci_kmeans.labels_, name='K-means'))"
    ]
   },
   {
@@ -3179,12 +3145,11 @@
    "id": "b09ceeab",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:17.955945Z",
-     "iopub.status.busy": "2023-07-26T05:18:17.954718Z",
-     "iopub.status.idle": "2023-07-26T05:18:18.488264Z",
-     "shell.execute_reply": "2023-07-26T05:18:18.487809Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:38.528901Z",
+     "iopub.status.busy": "2023-07-26T19:30:38.528743Z",
+     "iopub.status.idle": "2023-07-26T19:30:38.969519Z",
+     "shell.execute_reply": "2023-07-26T19:30:38.969239Z"
+    }
    },
    "outputs": [
     {
@@ -3374,26 +3339,15 @@
     "pca_labels = pd.Series(cut_tree(linkage_pca,\n",
     "                                n_clusters=4).reshape(-1),\n",
     "                       name='Complete-PCA')\n",
-    "pd.crosstab(nci_labs['label'], pca_labels)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e1733b5",
-   "metadata": {},
-   "source": [
-    "\n",
-    " \n",
-    " \n",
-    "\n"
+    "pd.crosstab(nci_labs['label'], pca_labels)"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3405,7 +3359,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch13-multiple-lab.ipynb b/Ch13-multiple-lab.ipynb
index 65fb5bb..05a9cc5 100644
--- a/Ch13-multiple-lab.ipynb
+++ b/Ch13-multiple-lab.ipynb
@@ -5,11 +5,9 @@
    "id": "75b2d75c",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 13\n",
     "\n",
-    "# Lab: Multiple Testing\n",
-    " "
+    "# Lab: Multiple Testing"
    ]
   },
   {
@@ -26,10 +24,10 @@
    "id": "1f928b2d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:21.791802Z",
-     "iopub.status.busy": "2023-07-26T10:52:21.791660Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.360588Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.359991Z"
+     "iopub.execute_input": "2023-07-26T19:30:40.174249Z",
+     "iopub.status.busy": "2023-07-26T19:30:40.174147Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.295542Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.294997Z"
     }
    },
    "outputs": [],
@@ -38,7 +36,7 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import statsmodels.api as sm\n",
-    "from ISLP import load_data\n"
+    "from ISLP import load_data"
    ]
   },
   {
@@ -56,12 +54,11 @@
    "id": "eb4b32aa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.363356Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.362843Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.365980Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.365438Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:41.298057Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.297544Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.300143Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.299888Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -73,7 +70,7 @@
     "from statsmodels.stats.multicomp import \\\n",
     "     pairwise_tukeyhsd\n",
     "from statsmodels.stats.multitest import \\\n",
-    "     multipletests as mult_test\n"
+    "     multipletests as mult_test"
    ]
   },
   {
@@ -95,10 +92,10 @@
    "id": "e12ac0cd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.368340Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.368164Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.371202Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.370779Z"
+     "iopub.execute_input": "2023-07-26T19:30:41.301702Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.301592Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.303876Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.303617Z"
     }
    },
    "outputs": [],
@@ -106,7 +103,7 @@
     "rng = np.random.default_rng(12)\n",
     "X = rng.standard_normal((10, 100))\n",
     "true_mean = np.array([0.5]*50 + [0]*50)\n",
-    "X += true_mean[None,:]\n"
+    "X += true_mean[None,:]"
    ]
   },
   {
@@ -125,10 +122,10 @@
    "id": "04d0f49e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.373512Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.373319Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.378383Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.377960Z"
+     "iopub.execute_input": "2023-07-26T19:30:41.305411Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.305302Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.309554Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.309317Z"
     }
    },
    "outputs": [
@@ -145,7 +142,7 @@
    ],
    "source": [
     "result = ttest_1samp(X[:,0], 0)\n",
-    "result.pvalue\n"
+    "result.pvalue"
    ]
   },
   {
@@ -172,12 +169,11 @@
    "id": "d1f0c695",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.380819Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.380641Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.408797Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.408278Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:41.311030Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.310945Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.336675Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.336337Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -190,7 +186,7 @@
     "                          'Do not reject H0'])\n",
     "truth = pd.Categorical(true_mean == 0,\n",
     "                       categories=[True, False],\n",
-    "                       ordered=True)\n"
+    "                       ordered=True)"
    ]
   },
   {
@@ -208,12 +204,11 @@
    "id": "7a9594a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.411256Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.411053Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.422652Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.422286Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:41.338368Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.338270Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.347923Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.347609Z"
+    }
    },
    "outputs": [
     {
@@ -277,7 +272,7 @@
     "pd.crosstab(decision,\n",
     "            truth,\n",
     "     rownames=['Decision'],\n",
-    "     colnames=['H0'])\n"
+    "     colnames=['H0'])"
    ]
   },
   {
@@ -299,7 +294,7 @@
     "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."
    ]
   },
   {
@@ -308,12 +303,11 @@
    "id": "25f7fc5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.425157Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.424798Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.455752Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.455052Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:41.349462Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.349374Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.377229Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.376963Z"
+    }
    },
    "outputs": [
     {
@@ -389,15 +383,7 @@
     "pd.crosstab(decision,\n",
     "            truth,\n",
     "            rownames=['Decision'],\n",
-    "            colnames=['H0'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bb70c597",
-   "metadata": {},
-   "source": [
-    "    "
+    "            colnames=['H0'])"
    ]
   },
   {
@@ -420,10 +406,10 @@
    "id": "369b5bd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.458631Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.458434Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.710141Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.709635Z"
+     "iopub.execute_input": "2023-07-26T19:30:41.378742Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.378656Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.640684Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.639810Z"
     }
    },
    "outputs": [
@@ -449,7 +435,7 @@
     "ax.set_xlabel('Number of Hypotheses')\n",
     "ax.set_ylabel('Family-Wise Error Rate')\n",
     "ax.legend()\n",
-    "ax.axhline(0.05, c='k', ls='--');\n"
+    "ax.axhline(0.05, c='k', ls='--');"
    ]
   },
   {
@@ -476,10 +462,10 @@
    "id": "9ce7a19f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.713438Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.713214Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.754484Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.754038Z"
+     "iopub.execute_input": "2023-07-26T19:30:41.651427Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.646298Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.691862Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.691584Z"
     }
    },
    "outputs": [
@@ -500,7 +486,7 @@
     "fund_mini_pvals = np.empty(5)\n",
     "for i in range(5):\n",
     "    fund_mini_pvals[i] = ttest_1samp(fund_mini.iloc[:,i], 0).pvalue\n",
-    "fund_mini_pvals\n"
+    "fund_mini_pvals"
    ]
   },
   {
@@ -544,12 +530,11 @@
    "id": "de6cffed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.756665Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.756493Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.759438Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.759028Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:41.693537Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.693432Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.695845Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.695583Z"
+    }
    },
    "outputs": [
     {
@@ -565,7 +550,7 @@
    ],
    "source": [
     "reject, bonf = mult_test(fund_mini_pvals, method = \"bonferroni\")[:2]\n",
-    "reject\n"
+    "reject"
    ]
   },
   {
@@ -583,10 +568,10 @@
    "id": "0de71500",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.761459Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.761287Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.764317Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.763952Z"
+     "iopub.execute_input": "2023-07-26T19:30:41.697297Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.697186Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.699576Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.699333Z"
     }
    },
    "outputs": [
@@ -603,7 +588,7 @@
     }
    ],
    "source": [
-    "bonf, np.minimum(fund_mini_pvals * 5, 1)\n"
+    "bonf, np.minimum(fund_mini_pvals * 5, 1)"
    ]
   },
   {
@@ -625,12 +610,11 @@
    "id": "f7e87bdb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.766793Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.766606Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.810152Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.809736Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:41.700975Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.700886Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.744972Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.744690Z"
+    }
    },
    "outputs": [
     {
@@ -646,7 +630,7 @@
     }
    ],
    "source": [
-    "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]\n"
+    "mult_test(fund_mini_pvals, method = \"holm\", alpha=0.05)[:2]"
    ]
   },
   {
@@ -664,12 +648,11 @@
    "id": "e88be376",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.812384Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.812232Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.815971Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.815576Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:41.746473Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.746386Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.749362Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.749117Z"
+    }
    },
    "outputs": [
     {
@@ -689,7 +672,7 @@
     }
    ],
    "source": [
-    "fund_mini.mean()\n"
+    "fund_mini.mean()"
    ]
   },
   {
@@ -708,10 +691,10 @@
    "id": "41149af6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.817997Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.817852Z",
-     "iopub.status.idle": "2023-07-26T10:52:23.821301Z",
-     "shell.execute_reply": "2023-07-26T10:52:23.820976Z"
+     "iopub.execute_input": "2023-07-26T19:30:41.750766Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.750681Z",
+     "iopub.status.idle": "2023-07-26T19:30:41.753275Z",
+     "shell.execute_reply": "2023-07-26T19:30:41.753015Z"
     }
    },
    "outputs": [
@@ -728,7 +711,7 @@
    ],
    "source": [
     "ttest_rel(fund_mini['Manager1'],\n",
-    "          fund_mini['Manager2']).pvalue\n"
+    "          fund_mini['Manager2']).pvalue"
    ]
   },
   {
@@ -760,12 +743,11 @@
    "id": "61aabda7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:23.823367Z",
-     "iopub.status.busy": "2023-07-26T10:52:23.823190Z",
-     "iopub.status.idle": "2023-07-26T10:52:24.308715Z",
-     "shell.execute_reply": "2023-07-26T10:52:24.308222Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:41.754673Z",
+     "iopub.status.busy": "2023-07-26T19:30:41.754580Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.239556Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.239231Z"
+    }
    },
    "outputs": [
     {
@@ -794,7 +776,7 @@
     "returns = np.hstack([fund_mini.iloc[:,i] for i in range(5)])\n",
     "managers = np.hstack([[i+1]*50 for i in range(5)])\n",
     "tukey = pairwise_tukeyhsd(returns, managers)\n",
-    "print(tukey.summary())\n"
+    "print(tukey.summary())"
    ]
   },
   {
@@ -820,10 +802,10 @@
    "id": "cbcad4de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:24.311170Z",
-     "iopub.status.busy": "2023-07-26T10:52:24.310960Z",
-     "iopub.status.idle": "2023-07-26T10:52:24.410742Z",
-     "shell.execute_reply": "2023-07-26T10:52:24.410212Z"
+     "iopub.execute_input": "2023-07-26T19:30:42.241228Z",
+     "iopub.status.busy": "2023-07-26T19:30:42.241113Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.322033Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.321731Z"
     }
    },
    "outputs": [
@@ -840,7 +822,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(8,8))\n",
-    "tukey.plot_simultaneous(ax=ax);\n"
+    "tukey.plot_simultaneous(ax=ax);"
    ]
   },
   {
@@ -861,17 +843,17 @@
    "id": "b5842190",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:24.413638Z",
-     "iopub.status.busy": "2023-07-26T10:52:24.413455Z",
-     "iopub.status.idle": "2023-07-26T10:52:24.827343Z",
-     "shell.execute_reply": "2023-07-26T10:52:24.826609Z"
+     "iopub.execute_input": "2023-07-26T19:30:42.323675Z",
+     "iopub.status.busy": "2023-07-26T19:30:42.323560Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.738874Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.738524Z"
     }
    },
    "outputs": [],
    "source": [
     "fund_pvalues = np.empty(2000)\n",
     "for i, manager in enumerate(Fund.columns):\n",
-    "    fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue\n"
+    "    fund_pvalues[i] = ttest_1samp(Fund[manager], 0).pvalue"
    ]
   },
   {
@@ -890,10 +872,10 @@
    "id": "7c9d8bed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:24.830214Z",
-     "iopub.status.busy": "2023-07-26T10:52:24.830015Z",
-     "iopub.status.idle": "2023-07-26T10:52:24.834953Z",
-     "shell.execute_reply": "2023-07-26T10:52:24.834464Z"
+     "iopub.execute_input": "2023-07-26T19:30:42.740722Z",
+     "iopub.status.busy": "2023-07-26T19:30:42.740600Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.743374Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.743108Z"
     }
    },
    "outputs": [
@@ -911,7 +893,7 @@
    ],
    "source": [
     "fund_qvalues = mult_test(fund_pvalues, method = \"fdr_bh\")[1]\n",
-    "fund_qvalues[:10]\n"
+    "fund_qvalues[:10]"
    ]
   },
   {
@@ -935,12 +917,11 @@
    "id": "bfa39f7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:24.837627Z",
-     "iopub.status.busy": "2023-07-26T10:52:24.837419Z",
-     "iopub.status.idle": "2023-07-26T10:52:24.840834Z",
-     "shell.execute_reply": "2023-07-26T10:52:24.840316Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:42.744900Z",
+     "iopub.status.busy": "2023-07-26T19:30:42.744792Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.747030Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.746764Z"
+    }
    },
    "outputs": [
     {
@@ -955,7 +936,7 @@
     }
    ],
    "source": [
-    "(fund_qvalues <= 0.1).sum()\n"
+    "(fund_qvalues <= 0.1).sum()"
    ]
   },
   {
@@ -979,12 +960,11 @@
    "id": "70b69b47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:24.843470Z",
-     "iopub.status.busy": "2023-07-26T10:52:24.843237Z",
-     "iopub.status.idle": "2023-07-26T10:52:24.846889Z",
-     "shell.execute_reply": "2023-07-26T10:52:24.846276Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:42.748394Z",
+     "iopub.status.busy": "2023-07-26T19:30:42.748301Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.750369Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.750115Z"
+    }
    },
    "outputs": [
     {
@@ -999,7 +979,7 @@
     }
    ],
    "source": [
-    "(fund_pvalues <= 0.1 / 2000).sum()\n"
+    "(fund_pvalues <= 0.1 / 2000).sum()"
    ]
   },
   {
@@ -1029,10 +1009,10 @@
    "id": "4c0ddea1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:24.849388Z",
-     "iopub.status.busy": "2023-07-26T10:52:24.849238Z",
-     "iopub.status.idle": "2023-07-26T10:52:24.852847Z",
-     "shell.execute_reply": "2023-07-26T10:52:24.852354Z"
+     "iopub.execute_input": "2023-07-26T19:30:42.751896Z",
+     "iopub.status.busy": "2023-07-26T19:30:42.751792Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.754281Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.754006Z"
     }
    },
    "outputs": [],
@@ -1046,7 +1026,7 @@
     "    sorted_set_ = np.arange(sorted_set_.max())\n",
     "else:\n",
     "    selected_ = []\n",
-    "    sorted_set_ = []\n"
+    "    sorted_set_ = []"
    ]
   },
   {
@@ -1063,12 +1043,11 @@
    "id": "0314eac9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:24.855721Z",
-     "iopub.status.busy": "2023-07-26T10:52:24.855334Z",
-     "iopub.status.idle": "2023-07-26T10:52:25.156072Z",
-     "shell.execute_reply": "2023-07-26T10:52:25.155438Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:42.755678Z",
+     "iopub.status.busy": "2023-07-26T19:30:42.755585Z",
+     "iopub.status.idle": "2023-07-26T19:30:42.999460Z",
+     "shell.execute_reply": "2023-07-26T19:30:42.998935Z"
+    }
    },
    "outputs": [
     {
@@ -1091,7 +1070,7 @@
     "ax.set_ylabel('P-Value')\n",
     "ax.set_xlabel('Index')\n",
     "ax.scatter(sorted_set_+1, sorted_[sorted_set_], c='r', s=20)\n",
-    "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);\n"
+    "ax.axline((0, 0), (1,q/m), c='k', ls='--', linewidth=3);"
    ]
   },
   {
@@ -1113,12 +1092,11 @@
    "id": "b59b8137",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:25.158998Z",
-     "iopub.status.busy": "2023-07-26T10:52:25.158769Z",
-     "iopub.status.idle": "2023-07-26T10:52:25.243020Z",
-     "shell.execute_reply": "2023-07-26T10:52:25.242512Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:43.001457Z",
+     "iopub.status.busy": "2023-07-26T19:30:43.001301Z",
+     "iopub.status.idle": "2023-07-26T19:30:43.079281Z",
+     "shell.execute_reply": "2023-07-26T19:30:43.078814Z"
+    }
    },
    "outputs": [
     {
@@ -1140,7 +1118,7 @@
     "Khan = load_data('Khan')      \n",
     "D = pd.concat([Khan['xtrain'], Khan['xtest']])\n",
     "D['Y'] = pd.concat([Khan['ytrain'], Khan['ytest']])\n",
-    "D['Y'].value_counts()\n"
+    "D['Y'].value_counts()"
    ]
   },
   {
@@ -1164,12 +1142,11 @@
    "id": "96fb2f61",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:25.245359Z",
-     "iopub.status.busy": "2023-07-26T10:52:25.245172Z",
-     "iopub.status.idle": "2023-07-26T10:52:25.250139Z",
-     "shell.execute_reply": "2023-07-26T10:52:25.249644Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:43.081705Z",
+     "iopub.status.busy": "2023-07-26T19:30:43.081539Z",
+     "iopub.status.idle": "2023-07-26T19:30:43.086400Z",
+     "shell.execute_reply": "2023-07-26T19:30:43.086081Z"
+    }
    },
    "outputs": [
     {
@@ -1190,7 +1167,7 @@
     "observedT, pvalue = ttest_ind(D2[gene_11],\n",
     "                              D4[gene_11],\n",
     "                              equal_var=True)\n",
-    "observedT, pvalue\n"
+    "observedT, pvalue"
    ]
   },
   {
@@ -1217,12 +1194,11 @@
    "id": "fdc229fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:25.252510Z",
-     "iopub.status.busy": "2023-07-26T10:52:25.252319Z",
-     "iopub.status.idle": "2023-07-26T10:52:26.208474Z",
-     "shell.execute_reply": "2023-07-26T10:52:26.207894Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:43.088801Z",
+     "iopub.status.busy": "2023-07-26T19:30:43.088456Z",
+     "iopub.status.idle": "2023-07-26T19:30:45.428003Z",
+     "shell.execute_reply": "2023-07-26T19:30:45.427711Z"
+    }
    },
    "outputs": [
     {
@@ -1248,7 +1224,7 @@
     "                       D_null[n_:],\n",
     "                       equal_var=True)\n",
     "    Tnull[b] = ttest_.statistic\n",
-    "(np.abs(Tnull) > np.abs(observedT)).mean()\n"
+    "(np.abs(Tnull) > np.abs(observedT)).mean()"
    ]
   },
   {
@@ -1268,12 +1244,11 @@
    "id": "e3894695",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:26.210995Z",
-     "iopub.status.busy": "2023-07-26T10:52:26.210826Z",
-     "iopub.status.idle": "2023-07-26T10:52:26.458716Z",
-     "shell.execute_reply": "2023-07-26T10:52:26.458144Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:45.429626Z",
+     "iopub.status.busy": "2023-07-26T19:30:45.429511Z",
+     "iopub.status.idle": "2023-07-26T19:30:45.644637Z",
+     "shell.execute_reply": "2023-07-26T19:30:45.644276Z"
+    }
    },
    "outputs": [
     {
@@ -1302,7 +1277,7 @@
     "           c='b',\n",
     "           label='Observed')\n",
     "ax.legend()\n",
-    "ax.set_xlabel(\"Null Distribution of Test Statistic\");\n"
+    "ax.set_xlabel(\"Null Distribution of Test Statistic\");"
    ]
   },
   {
@@ -1328,10 +1303,10 @@
    "id": "3b7392cb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T10:52:26.461754Z",
-     "iopub.status.busy": "2023-07-26T10:52:26.461349Z",
-     "iopub.status.idle": "2023-07-26T12:21:43.650925Z",
-     "shell.execute_reply": "2023-07-26T12:21:43.650584Z"
+     "iopub.execute_input": "2023-07-26T19:30:45.646419Z",
+     "iopub.status.busy": "2023-07-26T19:30:45.646303Z",
+     "iopub.status.idle": "2023-07-26T19:34:40.348681Z",
+     "shell.execute_reply": "2023-07-26T19:34:40.347928Z"
     }
    },
    "outputs": [],
@@ -1353,7 +1328,7 @@
     "        ttest_ = ttest_ind(D_null[:n_],\n",
     "                           D_null[n_:],\n",
     "                           equal_var=True)\n",
-    "        Tnull_vals[j,b] = ttest_.statistic\n"
+    "        Tnull_vals[j,b] = ttest_.statistic"
    ]
   },
   {
@@ -1374,10 +1349,10 @@
    "id": "cac15616",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T12:21:43.653105Z",
-     "iopub.status.busy": "2023-07-26T12:21:43.652856Z",
-     "iopub.status.idle": "2023-07-26T12:21:43.895651Z",
-     "shell.execute_reply": "2023-07-26T12:21:43.895292Z"
+     "iopub.execute_input": "2023-07-26T19:34:40.352699Z",
+     "iopub.status.busy": "2023-07-26T19:34:40.352545Z",
+     "iopub.status.idle": "2023-07-26T19:34:40.476265Z",
+     "shell.execute_reply": "2023-07-26T19:34:40.475900Z"
     }
    },
    "outputs": [],
@@ -1389,7 +1364,7 @@
     "   V = np.sum(np.abs(Tnull_vals) >= cutoffs[j]) / B\n",
     "   Rs[j] = R\n",
     "   Vs[j] = V\n",
-    "   FDRs[j] = V / R\n"
+    "   FDRs[j] = V / R"
    ]
   },
   {
@@ -1415,10 +1390,10 @@
    "id": "9661eb10",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T12:21:43.897943Z",
-     "iopub.status.busy": "2023-07-26T12:21:43.897797Z",
-     "iopub.status.idle": "2023-07-26T12:21:43.901002Z",
-     "shell.execute_reply": "2023-07-26T12:21:43.900637Z"
+     "iopub.execute_input": "2023-07-26T19:34:40.478059Z",
+     "iopub.status.busy": "2023-07-26T19:34:40.477950Z",
+     "iopub.status.idle": "2023-07-26T19:34:40.481159Z",
+     "shell.execute_reply": "2023-07-26T19:34:40.480856Z"
     }
    },
    "outputs": [
@@ -1452,7 +1427,7 @@
     }
    ],
    "source": [
-    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])\n"
+    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.1].min()])"
    ]
   },
   {
@@ -1470,10 +1445,10 @@
    "id": "18ad4900",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T12:21:43.902778Z",
-     "iopub.status.busy": "2023-07-26T12:21:43.902684Z",
-     "iopub.status.idle": "2023-07-26T12:21:43.905535Z",
-     "shell.execute_reply": "2023-07-26T12:21:43.905228Z"
+     "iopub.execute_input": "2023-07-26T19:34:40.483066Z",
+     "iopub.status.busy": "2023-07-26T19:34:40.482980Z",
+     "iopub.status.idle": "2023-07-26T19:34:40.485451Z",
+     "shell.execute_reply": "2023-07-26T19:34:40.485202Z"
     }
    },
    "outputs": [
@@ -1519,7 +1494,7 @@
     }
    ],
    "source": [
-    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])\n"
+    "sorted(idx[np.abs(T_vals) >= cutoffs[FDRs < 0.2].min()])"
    ]
   },
   {
@@ -1538,12 +1513,11 @@
    "id": "28c276b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T12:21:43.907278Z",
-     "iopub.status.busy": "2023-07-26T12:21:43.907150Z",
-     "iopub.status.idle": "2023-07-26T12:21:43.988689Z",
-     "shell.execute_reply": "2023-07-26T12:21:43.988352Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:34:40.487079Z",
+     "iopub.status.busy": "2023-07-26T19:34:40.486999Z",
+     "iopub.status.idle": "2023-07-26T19:34:40.563798Z",
+     "shell.execute_reply": "2023-07-26T19:34:40.563460Z"
+    }
    },
    "outputs": [
     {
@@ -1561,23 +1535,15 @@
     "fig, ax = plt.subplots()\n",
     "ax.plot(Rs, FDRs, 'b', linewidth=3)\n",
     "ax.set_xlabel(\"Number of Rejections\")\n",
-    "ax.set_ylabel(\"False Discovery Rate\");\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e4b5d621",
-   "metadata": {},
-   "source": [
-    "\n"
+    "ax.set_ylabel(\"False Discovery Rate\");"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1589,7 +1555,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch2-statlearn-lab.ipynb b/Ch2-statlearn-lab.ipynb
index d0344cd..19d50c4 100644
--- a/Ch2-statlearn-lab.ipynb
+++ b/Ch2-statlearn-lab.ipynb
@@ -5,11 +5,9 @@
    "id": "46456e09",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 2\n",
     "\n",
-    "# Lab: Introduction to Python\n",
-    "\n"
+    "# Lab: Introduction to Python"
    ]
   },
   {
@@ -28,7 +26,7 @@
     "To run the labs in this book, you will need two things:\n",
     "\n",
     "* An installation of `Python3`, which is the specific version of `Python`  used in the labs. \n",
-    "* Access to  `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*. "
+    "* Access to  `Jupyter`, a very popular `Python` interface that runs code through a file called a *notebook*."
    ]
   },
   {
@@ -36,7 +34,7 @@
    "id": "f922cc9e",
    "metadata": {},
    "source": [
-    "You can download and install  `Python3`   by following the instructions available at [anaconda.com](http://anaconda.com). "
+    "You can download and install  `Python3`   by following the instructions available at [anaconda.com](http://anaconda.com)."
    ]
   },
   {
@@ -58,7 +56,7 @@
     "To run this lab, download the file `Ch2-statlearn-lab.ipynb` from the `Python` resources page. \n",
     "Now run the following code at the command line: `jupyter lab Ch2-statlearn-lab.ipynb`.\n",
     "\n",
-    "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell.\n"
+    "If you're using Windows, you can use the `start menu` to access `anaconda`, and follow the links. For example, to install `ISLP` and run this lab, you can run the same code above in an `anaconda` shell."
    ]
   },
   {
@@ -66,7 +64,7 @@
    "id": "7a44fbd9",
    "metadata": {},
    "source": [
-    "## Basic Commands\n"
+    "## Basic Commands"
    ]
   },
   {
@@ -75,10 +73,7 @@
    "metadata": {},
    "source": [
     "In this lab, we will introduce some simple `Python` commands. \n",
-    " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/). \n",
-    "\n",
-    "\n",
-    " \n"
+    " For more resources about `Python` in general, readers may want to consult the tutorial at [docs.python.org/3/tutorial/](https://docs.python.org/3/tutorial/)."
    ]
   },
   {
@@ -118,7 +113,7 @@
     }
    ],
    "source": [
-    "print('fit a model with', 11, 'variables')\n"
+    "print('fit a model with', 11, 'variables')"
    ]
   },
   {
@@ -143,7 +138,7 @@
    },
    "outputs": [],
    "source": [
-    "print?\n"
+    "print?"
    ]
   },
   {
@@ -179,7 +174,7 @@
     }
    ],
    "source": [
-    "3 + 5\n"
+    "3 + 5"
    ]
   },
   {
@@ -219,7 +214,7 @@
     }
    ],
    "source": [
-    "\"hello\" + \" \" + \"world\"\n"
+    "\"hello\" + \" \" + \"world\""
    ]
   },
   {
@@ -229,7 +224,7 @@
    "source": [
     " A string is actually a type of *sequence*: this is a generic term for an ordered list. \n",
     " The three most important types of sequences are lists, tuples, and strings.  \n",
-    "We introduce lists now. "
+    "We introduce lists now."
    ]
   },
   {
@@ -269,7 +264,7 @@
    ],
    "source": [
     "x = [3, 4, 5]\n",
-    "x\n"
+    "x"
    ]
   },
   {
@@ -310,7 +305,7 @@
    ],
    "source": [
     "y = [4, 9, 7]\n",
-    "x + y\n"
+    "x + y"
    ]
   },
   {
@@ -321,8 +316,7 @@
     "The result may appear slightly counterintuitive: why did `Python` not add the entries of the lists\n",
     "element-by-element? \n",
     " In `Python`, lists hold *arbitrary* objects, and  are added using  *concatenation*. \n",
-    " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`. \n",
-    " "
+    " In fact, concatenation is the behavior that we saw earlier when we entered `\"hello\" + \" \" + \"world\"`."
    ]
   },
   {
@@ -335,7 +329,7 @@
     "functionality comes from other packages, notably `numpy`\n",
     "and `pandas`. \n",
     "In the next section, we will introduce the  `numpy` package. More information about `numpy` can be found  at \n",
-    "[docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html).\n"
+    "[docs.scipy.org/doc/numpy/user/quickstart.html](https://docs.scipy.org/doc/numpy/user/quickstart.html)."
    ]
   },
   {
@@ -347,7 +341,7 @@
     "\n",
     "As mentioned earlier, this book makes use of functionality   that is contained in the `numpy` \n",
     " *library*, or *package*. A package is a collection of modules that are not necessarily included in \n",
-    " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*. "
+    " the base `Python` distribution. The name `numpy` is an abbreviation for *numerical Python*."
    ]
   },
   {
@@ -368,8 +362,7 @@
      "iopub.status.busy": "2023-07-26T05:29:13.687702Z",
      "iopub.status.idle": "2023-07-26T05:29:13.902613Z",
      "shell.execute_reply": "2023-07-26T05:29:13.901721Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [],
    "source": [
@@ -404,8 +397,7 @@
      "iopub.status.busy": "2023-07-26T05:29:13.907922Z",
      "iopub.status.idle": "2023-07-26T05:29:13.914563Z",
      "shell.execute_reply": "2023-07-26T05:29:13.912356Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [],
    "source": [
@@ -421,7 +413,7 @@
     "Note that if you forgot to run the `import numpy as np` command earlier, then\n",
     "you will encounter an error in calling the `np.array()` function in the previous line. \n",
     " The syntax `np.array()` indicates that the function being called\n",
-    "is part of the `numpy` package, which we have abbreviated as `np`. "
+    "is part of the `numpy` package, which we have abbreviated as `np`."
    ]
   },
   {
@@ -430,7 +422,7 @@
    "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",
-    " when we tried to add two lists without using `numpy`. "
+    " when we tried to add two lists without using `numpy`."
    ]
   },
   {
@@ -443,8 +435,7 @@
      "iopub.status.busy": "2023-07-26T05:29:13.919275Z",
      "iopub.status.idle": "2023-07-26T05:29:13.929618Z",
      "shell.execute_reply": "2023-07-26T05:29:13.928417Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -462,22 +453,13 @@
     "x + y"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "06a14452",
-   "metadata": {},
-   "source": [
-    "    \n",
-    " \n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "33df3775",
    "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",
-    "We can create a two-dimensional array as follows. "
+    "We can create a two-dimensional array as follows."
    ]
   },
   {
@@ -490,8 +472,7 @@
      "iopub.status.busy": "2023-07-26T05:29:13.934172Z",
      "iopub.status.idle": "2023-07-26T05:29:13.941331Z",
      "shell.execute_reply": "2023-07-26T05:29:13.939888Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -511,15 +492,6 @@
     "x"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "c04d19a8",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "86de84a5",
@@ -528,7 +500,7 @@
     "The object `x` has several \n",
     "*attributes*, or associated objects. To access an attribute of `x`, we type `x.attribute`, where we replace `attribute`\n",
     "with the name of the attribute. \n",
-    "For instance, we can access the `ndim` attribute of  `x` as follows. "
+    "For instance, we can access the `ndim` attribute of  `x` as follows."
    ]
   },
   {
@@ -579,8 +551,7 @@
      "iopub.status.busy": "2023-07-26T05:29:13.957674Z",
      "iopub.status.idle": "2023-07-26T05:29:13.965373Z",
      "shell.execute_reply": "2023-07-26T05:29:13.964551Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -606,7 +577,7 @@
     "Why is `x` comprised of integers? This is because we created `x` by passing in exclusively integers to the `np.array()` function.\n",
     "  If\n",
     "we had passed in any decimals, then we would have obtained an array of\n",
-    "*floating point numbers* (i.e. real-valued numbers). "
+    "*floating point numbers* (i.e. real-valued numbers)."
    ]
   },
   {
@@ -619,8 +590,7 @@
      "iopub.status.busy": "2023-07-26T05:29:13.970020Z",
      "iopub.status.idle": "2023-07-26T05:29:13.977698Z",
      "shell.execute_reply": "2023-07-26T05:29:13.976971Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -635,7 +605,7 @@
     }
    ],
    "source": [
-    "np.array([[1, 2], [3.0, 4]]).dtype\n"
+    "np.array([[1, 2], [3.0, 4]]).dtype"
    ]
   },
   {
@@ -645,7 +615,7 @@
    "source": [
     "Typing `fun?` will cause `Python` to display \n",
     "documentation associated with the function `fun`, if it exists.\n",
-    "We can try this for `np.array()`. "
+    "We can try this for `np.array()`."
    ]
   },
   {
@@ -658,12 +628,11 @@
      "iopub.status.busy": "2023-07-26T05:29:13.982433Z",
      "iopub.status.idle": "2023-07-26T05:29:13.987748Z",
      "shell.execute_reply": "2023-07-26T05:29:13.986808Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [],
    "source": [
-    "np.array?\n"
+    "np.array?"
    ]
   },
   {
@@ -684,8 +653,7 @@
      "iopub.status.busy": "2023-07-26T05:29:13.996561Z",
      "iopub.status.idle": "2023-07-26T05:29:14.002488Z",
      "shell.execute_reply": "2023-07-26T05:29:14.001948Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -700,7 +668,7 @@
     }
    ],
    "source": [
-    "np.array([[1, 2], [3, 4]], float).dtype\n"
+    "np.array([[1, 2], [3, 4]], float).dtype"
    ]
   },
   {
@@ -722,8 +690,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.004832Z",
      "iopub.status.idle": "2023-07-26T05:29:14.007701Z",
      "shell.execute_reply": "2023-07-26T05:29:14.007253Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -738,7 +705,7 @@
     }
    ],
    "source": [
-    "x.shape\n"
+    "x.shape"
    ]
   },
   {
@@ -765,8 +732,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.009950Z",
      "iopub.status.idle": "2023-07-26T05:29:14.013139Z",
      "shell.execute_reply": "2023-07-26T05:29:14.012702Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -790,7 +756,7 @@
    "id": "72373ccf",
    "metadata": {},
    "source": [
-    "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function. "
+    "We could also sum the elements of `x` by passing in `x` as an argument to the `np.sum()` function."
    ]
   },
   {
@@ -803,8 +769,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.015347Z",
      "iopub.status.idle": "2023-07-26T05:29:14.018540Z",
      "shell.execute_reply": "2023-07-26T05:29:14.017992Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -868,7 +833,7 @@
     "x = np.array([1, 2, 3, 4, 5, 6])\n",
     "print('beginning x:\\n', x)\n",
     "x_reshape = x.reshape((2, 3))\n",
-    "print('reshaped x:\\n', x_reshape)\n"
+    "print('reshaped x:\\n', x_reshape)"
    ]
   },
   {
@@ -877,7 +842,7 @@
    "metadata": {},
    "source": [
     "The previous output reveals that `numpy` arrays are specified as a sequence\n",
-    "of *rows*. This is  called *row-major ordering*, as opposed to *column-major ordering*. "
+    "of *rows*. This is  called *row-major ordering*, as opposed to *column-major ordering*."
    ]
   },
   {
@@ -900,8 +865,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.026156Z",
      "iopub.status.idle": "2023-07-26T05:29:14.029404Z",
      "shell.execute_reply": "2023-07-26T05:29:14.028872Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -925,7 +889,7 @@
    "metadata": {},
    "source": [
     "Similarly, `x_reshape[1,2]` yields the element in the second row and the third column \n",
-    "of `x_reshape`. "
+    "of `x_reshape`."
    ]
   },
   {
@@ -938,8 +902,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.031560Z",
      "iopub.status.idle": "2023-07-26T05:29:14.034395Z",
      "shell.execute_reply": "2023-07-26T05:29:14.033993Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -965,8 +928,7 @@
     "Similarly, `x[2]` yields the\n",
     "third entry of `x`. \n",
     "\n",
-    "Now, let's modify the top left element of `x_reshape`.  To our surprise, we discover that the first element of `x` has been modified as well!\n",
-    "\n"
+    "Now, let's modify the top left element of `x_reshape`.  To our surprise, we discover that the first element of `x` has been modified as well!"
    ]
   },
   {
@@ -1004,7 +966,7 @@
     "print('x_reshape before we modify x_reshape:\\n', x_reshape)\n",
     "x_reshape[0, 0] = 5\n",
     "print('x_reshape after we modify its top left element:\\n', x_reshape)\n",
-    "print('x after we modify top left element of x_reshape:\\n', x)\n"
+    "print('x after we modify top left element of x_reshape:\\n', x)"
    ]
   },
   {
@@ -1012,10 +974,7 @@
    "id": "10ff8c21",
    "metadata": {},
    "source": [
-    "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory.\n",
-    " \n",
-    "\n",
-    "    "
+    "Modifying `x_reshape` also modified `x` because the two objects occupy the same space in memory."
    ]
   },
   {
@@ -1037,8 +996,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.042111Z",
      "iopub.status.idle": "2023-07-26T05:29:14.226949Z",
      "shell.execute_reply": "2023-07-26T05:29:14.226329Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -1055,7 +1013,7 @@
    ],
    "source": [
     "my_tuple = (3, 4, 5)\n",
-    "my_tuple[0] = 2\n"
+    "my_tuple[0] = 2"
    ]
   },
   {
@@ -1064,7 +1022,7 @@
    "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",
-    "The  `ndim` attribute yields the number of dimensions, and `T` provides its transpose. "
+    "The  `ndim` attribute yields the number of dimensions, and `T` provides its transpose."
    ]
   },
   {
@@ -1096,7 +1054,7 @@
     }
    ],
    "source": [
-    "x_reshape.shape, x_reshape.ndim, x_reshape.T\n"
+    "x_reshape.shape, x_reshape.ndim, x_reshape.T"
    ]
   },
   {
@@ -1108,7 +1066,7 @@
     " \n",
     "We will often want to apply functions to arrays. \n",
     "For instance, we can compute the\n",
-    "square root of the entries using the `np.sqrt()` function: "
+    "square root of the entries using the `np.sqrt()` function:"
    ]
   },
   {
@@ -1137,7 +1095,7 @@
     }
    ],
    "source": [
-    "np.sqrt(x)\n"
+    "np.sqrt(x)"
    ]
   },
   {
@@ -1173,7 +1131,7 @@
     }
    ],
    "source": [
-    "x**2\n"
+    "x**2"
    ]
   },
   {
@@ -1194,8 +1152,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.246080Z",
      "iopub.status.idle": "2023-07-26T05:29:14.249174Z",
      "shell.execute_reply": "2023-07-26T05:29:14.248827Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -1211,7 +1168,7 @@
     }
    ],
    "source": [
-    "x**0.5\n"
+    "x**0.5"
    ]
   },
   {
@@ -1228,7 +1185,7 @@
     " By default, this function will generate random normal variable(s) with mean (`loc`) $0$ and standard deviation (`scale`) $1$; furthermore, \n",
     " a single random variable will be generated unless the argument to `size` is changed. \n",
     "\n",
-    "We now generate 50 independent random variables from a $N(0,1)$ distribution. "
+    "We now generate 50 independent random variables from a $N(0,1)$ distribution."
    ]
   },
   {
@@ -1266,7 +1223,7 @@
    ],
    "source": [
     "x = np.random.normal(size=50)\n",
-    "x\n"
+    "x"
    ]
   },
   {
@@ -1287,8 +1244,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.257502Z",
      "iopub.status.idle": "2023-07-26T05:29:14.259844Z",
      "shell.execute_reply": "2023-07-26T05:29:14.259473Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [],
    "source": [
@@ -1301,7 +1257,7 @@
    "metadata": {},
    "source": [
     "The `np.corrcoef()` function computes the correlation matrix between `x` and `y`. The off-diagonal elements give the \n",
-    "correlation between `x` and `y`. "
+    "correlation between `x` and `y`."
    ]
   },
   {
@@ -1354,8 +1310,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.269386Z",
      "iopub.status.idle": "2023-07-26T05:29:14.272569Z",
      "shell.execute_reply": "2023-07-26T05:29:14.271780Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -1369,15 +1324,7 @@
    ],
    "source": [
     "print(np.random.normal(scale=5, size=2))\n",
-    "print(np.random.normal(scale=5, size=2)) \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "477c992d",
-   "metadata": {},
-   "source": [
-    "    "
+    "print(np.random.normal(scale=5, size=2)) "
    ]
   },
   {
@@ -1451,8 +1398,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.282508Z",
      "iopub.status.idle": "2023-07-26T05:29:14.285616Z",
      "shell.execute_reply": "2023-07-26T05:29:14.285268Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -1472,14 +1418,6 @@
     "np.mean(y), y.mean()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d3d0e0fb",
-   "metadata": {},
-   "source": [
-    "    \n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 34,
@@ -1490,8 +1428,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.287646Z",
      "iopub.status.idle": "2023-07-26T05:29:14.290801Z",
      "shell.execute_reply": "2023-07-26T05:29:14.290395Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -1515,7 +1452,7 @@
    "metadata": {},
    "source": [
     "Notice that by default `np.var()` divides by the sample size $n$ rather\n",
-    "than $n-1$; see the `ddof` argument in `np.var?`.\n"
+    "than $n-1$; see the `ddof` argument in `np.var?`."
    ]
   },
   {
@@ -1552,7 +1489,7 @@
    "metadata": {},
    "source": [
     "The `np.mean()`,  `np.var()`, and `np.std()` functions can also be applied to the rows and columns of a matrix. \n",
-    "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums. "
+    "To see this, we construct a $10 \\times 3$ matrix of $N(0,1)$ random variables, and consider computing its row sums."
    ]
   },
   {
@@ -1598,7 +1535,7 @@
    "id": "f9028e1a",
    "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`. "
+    "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`."
    ]
   },
   {
@@ -1647,8 +1584,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.308171Z",
      "iopub.status.idle": "2023-07-26T05:29:14.311023Z",
      "shell.execute_reply": "2023-07-26T05:29:14.310704Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -1666,14 +1602,6 @@
     "X.mean(0)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "3b42d9c6",
-   "metadata": {},
-   "source": [
-    "    "
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e33fa3fc",
@@ -1736,7 +1664,7 @@
     "fig, ax = subplots(figsize=(8, 8))\n",
     "x = rng.standard_normal(100)\n",
     "y = rng.standard_normal(100)\n",
-    "ax.plot(x, y);\n"
+    "ax.plot(x, y);"
    ]
   },
   {
@@ -1797,8 +1725,7 @@
      "iopub.status.busy": "2023-07-26T05:29:14.908699Z",
      "iopub.status.idle": "2023-07-26T05:29:14.995469Z",
      "shell.execute_reply": "2023-07-26T05:29:14.994686Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -1824,7 +1751,7 @@
    "source": [
     "Different values\n",
     "of this additional argument can be used to produce different colored lines\n",
-    "as well as different linestyles. \n"
+    "as well as different linestyles."
    ]
   },
   {
@@ -1872,7 +1799,7 @@
     "Notice that in the code blocks above, we have ended\n",
     "the last line with a semicolon. This prevents `ax.plot(x, y)` from printing\n",
     "text  to the notebook. However, it does not prevent a plot from being produced. \n",
-    " If we omit the trailing semi-colon, then we obtain the following output:  "
+    " If we omit the trailing semi-colon, then we obtain the following output:"
    ]
   },
   {
@@ -1885,8 +1812,7 @@
      "iopub.status.busy": "2023-07-26T05:29:15.092487Z",
      "iopub.status.idle": "2023-07-26T05:29:15.212584Z",
      "shell.execute_reply": "2023-07-26T05:29:15.211918Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -1912,7 +1838,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.scatter(x, y, marker='o')\n"
+    "ax.scatter(x, y, marker='o')"
    ]
   },
   {
@@ -1922,10 +1848,7 @@
    "source": [
     "In what follows, we will use\n",
     " trailing semicolons whenever the text that would be output is not\n",
-    "germane to the discussion at hand.\n",
-    "\n",
-    "\n",
-    "\n"
+    "germane to the discussion at hand."
    ]
   },
   {
@@ -1934,8 +1857,7 @@
    "metadata": {},
    "source": [
     "To label our plot, we  make use of the `set_xlabel()`,  `set_ylabel()`, and  `set_title()` methods\n",
-    "of `ax`.\n",
-    "  "
+    "of `ax`."
    ]
   },
   {
@@ -1976,7 +1898,7 @@
    "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",
-    "  the size from `(8, 8)` to `(12, 3)`.\n"
+    "  the size from `(8, 8)` to `(12, 3)`."
    ]
   },
   {
@@ -1989,8 +1911,7 @@
      "iopub.status.busy": "2023-07-26T05:29:15.333963Z",
      "iopub.status.idle": "2023-07-26T05:29:15.405814Z",
      "shell.execute_reply": "2023-07-26T05:29:15.405400Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2010,14 +1931,6 @@
     "fig"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "4b70cd2e",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "3cf4ed3f",
@@ -2030,7 +1943,7 @@
     "situations, there is often a relationship between the axes in the plots. For example,\n",
     "all plots may have a common $x$-axis. The `subplots()` function can automatically handle\n",
     "this situation when passed the keyword argument `sharex=True`.\n",
-    "The `axes` object below is an array pointing to different plots in the figure. "
+    "The `axes` object below is an array pointing to different plots in the figure."
    ]
   },
   {
@@ -2043,8 +1956,7 @@
      "iopub.status.busy": "2023-07-26T05:29:15.408192Z",
      "iopub.status.idle": "2023-07-26T05:29:15.732292Z",
      "shell.execute_reply": "2023-07-26T05:29:15.731605Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2083,8 +1995,7 @@
      "iopub.status.busy": "2023-07-26T05:29:15.734697Z",
      "iopub.status.idle": "2023-07-26T05:29:15.959530Z",
      "shell.execute_reply": "2023-07-26T05:29:15.959066Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2111,9 +2022,7 @@
    "metadata": {},
    "source": [
     "Type  `subplots?` to learn more about \n",
-    "`subplots()`. \n",
-    "\n",
-    "\n"
+    "`subplots()`."
    ]
   },
   {
@@ -2136,13 +2045,12 @@
      "iopub.status.busy": "2023-07-26T05:29:15.962231Z",
      "iopub.status.idle": "2023-07-26T05:29:17.048215Z",
      "shell.execute_reply": "2023-07-26T05:29:17.047676Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [],
    "source": [
     "fig.savefig(\"Figure.png\", dpi=400)\n",
-    "fig.savefig(\"Figure.pdf\", dpi=200);\n"
+    "fig.savefig(\"Figure.pdf\", dpi=200);"
    ]
   },
   {
@@ -2150,7 +2058,7 @@
    "id": "b8e17a71",
    "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. "
+    "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."
    ]
   },
   {
@@ -2213,8 +2121,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.333339Z",
      "iopub.status.idle": "2023-07-26T05:29:17.452013Z",
      "shell.execute_reply": "2023-07-26T05:29:17.451505Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2233,7 +2140,7 @@
     "x = np.linspace(-np.pi, np.pi, 50)\n",
     "y = x\n",
     "f = np.multiply.outer(np.cos(y), 1 / (1 + x**2))\n",
-    "ax.contour(x, y, f);\n"
+    "ax.contour(x, y, f);"
    ]
   },
   {
@@ -2254,8 +2161,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.454485Z",
      "iopub.status.idle": "2023-07-26T05:29:17.604571Z",
      "shell.execute_reply": "2023-07-26T05:29:17.604143Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2300,8 +2206,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.607474Z",
      "iopub.status.idle": "2023-07-26T05:29:17.731227Z",
      "shell.execute_reply": "2023-07-26T05:29:17.730768Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -2317,7 +2222,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.imshow(f);\n"
+    "ax.imshow(f);"
    ]
   },
   {
@@ -2348,8 +2253,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.733437Z",
      "iopub.status.idle": "2023-07-26T05:29:17.736896Z",
      "shell.execute_reply": "2023-07-26T05:29:17.736428Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -2365,7 +2269,7 @@
    ],
    "source": [
     "seq1 = np.linspace(0, 10, 11)\n",
-    "seq1\n"
+    "seq1"
    ]
   },
   {
@@ -2404,7 +2308,7 @@
    ],
    "source": [
     "seq2 = np.arange(0, 10)\n",
-    "seq2\n"
+    "seq2"
    ]
   },
   {
@@ -2429,8 +2333,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.744099Z",
      "iopub.status.idle": "2023-07-26T05:29:17.747188Z",
      "shell.execute_reply": "2023-07-26T05:29:17.746807Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2454,7 +2357,7 @@
    "metadata": {},
    "source": [
     "In the code block above, the notation `3:6` is shorthand for  `slice(3,6)` when used inside\n",
-    "`[]`. "
+    "`[]`."
    ]
   },
   {
@@ -2482,7 +2385,7 @@
     }
    ],
    "source": [
-    "\"hello world\"[slice(3,6)]\n"
+    "\"hello world\"[slice(3,6)]"
    ]
   },
   {
@@ -2492,27 +2395,7 @@
    "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",
     " This also explains why the earlier `np.arange(0, 10)` command output only the integers from $0$ to $9$. \n",
-    "See the documentation `slice?` for useful options in creating slices. \n",
-    "\n",
-    "    \n",
-    "\n",
-    "\n",
-    "\n",
-    "    \n",
-    "\n",
-    "\n",
-    "    \n",
-    "\n",
-    " \n",
-    "\n",
-    "    \n",
-    "\n",
-    " \n",
-    "\n",
-    "    \n",
-    "\n",
-    "\n",
-    "    \n"
+    "See the documentation `slice?` for useful options in creating slices."
    ]
   },
   {
@@ -2553,7 +2436,7 @@
    ],
    "source": [
     "A = np.array(np.arange(16)).reshape((4, 4))\n",
-    "A\n"
+    "A"
    ]
   },
   {
@@ -2590,7 +2473,7 @@
     }
    ],
    "source": [
-    "A[1,2]\n"
+    "A[1,2]"
    ]
   },
   {
@@ -2632,7 +2515,7 @@
     }
    ],
    "source": [
-    "A[[1,3]]\n"
+    "A[[1,3]]"
    ]
   },
   {
@@ -2673,7 +2556,7 @@
     }
    ],
    "source": [
-    "A[:,[0,2]]\n"
+    "A[:,[0,2]]"
    ]
   },
   {
@@ -2711,7 +2594,7 @@
     }
    ],
    "source": [
-    "A[[1,3],[0,2]]\n"
+    "A[[1,3],[0,2]]"
    ]
   },
   {
@@ -2747,7 +2630,7 @@
     }
    ],
    "source": [
-    "np.array([A[1,0],A[3,2]])\n"
+    "np.array([A[1,0],A[3,2]])"
    ]
   },
   {
@@ -2784,7 +2667,7 @@
     }
    ],
    "source": [
-    "A[[1,3],[0,2,3]]\n"
+    "A[[1,3],[0,2,3]]"
    ]
   },
   {
@@ -2794,7 +2677,7 @@
    "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",
     "\n",
-    "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns.\n"
+    "One easy way to do this is as follows. We first create a submatrix by subsetting the rows of `A`, and then on the fly we make a further submatrix by subsetting its columns."
    ]
   },
   {
@@ -2807,8 +2690,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.815556Z",
      "iopub.status.idle": "2023-07-26T05:29:17.819079Z",
      "shell.execute_reply": "2023-07-26T05:29:17.818526Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2824,15 +2706,7 @@
     }
    ],
    "source": [
-    "A[[1,3]][:,[0,2]]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "00c8816e",
-   "metadata": {},
-   "source": [
-    "    "
+    "A[[1,3]][:,[0,2]]"
    ]
   },
   {
@@ -2856,8 +2730,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.821485Z",
      "iopub.status.idle": "2023-07-26T05:29:17.825110Z",
      "shell.execute_reply": "2023-07-26T05:29:17.824596Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -2874,7 +2747,7 @@
    ],
    "source": [
     "idx = np.ix_([1,3],[0,2,3])\n",
-    "A[idx]\n"
+    "A[idx]"
    ]
   },
   {
@@ -2899,8 +2772,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.827506Z",
      "iopub.status.idle": "2023-07-26T05:29:17.830968Z",
      "shell.execute_reply": "2023-07-26T05:29:17.830460Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2916,15 +2788,7 @@
     }
    ],
    "source": [
-    "A[1:4:2,0:3:2]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "63980dff",
-   "metadata": {},
-   "source": [
-    "    "
+    "A[1:4:2,0:3:2]"
    ]
   },
   {
@@ -2935,18 +2799,7 @@
     "Why are we able to retrieve a submatrix directly using slices but not using lists?\n",
     "Its because they are different `Python` types, and\n",
     "are treated differently by `numpy`.\n",
-    "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "    \n",
-    "\n",
-    " \n",
-    "\n",
-    "    \n",
-    "\n",
-    " "
+    "Slices can be used to extract objects from arbitrary sequences, such as strings, lists, and tuples, while the use of lists for indexing is more limited."
    ]
   },
   {
@@ -2956,7 +2809,7 @@
    "source": [
     "### Boolean Indexing\n",
     "In `numpy`, a *Boolean* is a type  that equals either   `True` or  `False` (also represented as $1$ and $0$, respectively).\n",
-    "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`. "
+    "The next line creates a vector of $0$'s, represented as Booleans, of length equal to the first dimension of `A`."
    ]
   },
   {
@@ -2969,8 +2822,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.833652Z",
      "iopub.status.idle": "2023-07-26T05:29:17.836642Z",
      "shell.execute_reply": "2023-07-26T05:29:17.836240Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -2994,7 +2846,7 @@
    "id": "f3007c8d",
    "metadata": {},
    "source": [
-    "We now set two of the elements to `True`. "
+    "We now set two of the elements to `True`."
    ]
   },
   {
@@ -3023,7 +2875,7 @@
    ],
    "source": [
     "keep_rows[[1,3]] = True\n",
-    "keep_rows\n"
+    "keep_rows"
    ]
   },
   {
@@ -3061,7 +2913,7 @@
     }
    ],
    "source": [
-    "np.all(keep_rows == np.array([0,1,0,1]))\n"
+    "np.all(keep_rows == np.array([0,1,0,1]))"
    ]
   },
   {
@@ -3079,7 +2931,7 @@
    "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",
-    "The former retrieves the first, second, first, and second rows of `A`. "
+    "The former retrieves the first, second, first, and second rows of `A`."
    ]
   },
   {
@@ -3110,7 +2962,7 @@
     }
    ],
    "source": [
-    "A[np.array([0,1,0,1])]\n"
+    "A[np.array([0,1,0,1])]"
    ]
   },
   {
@@ -3118,7 +2970,7 @@
    "id": "c0b18644",
    "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`. "
+    " By contrast, `keep_rows` retrieves only the second and fourth rows  of `A` --- i.e. the rows for which the Boolean equals `TRUE`."
    ]
   },
   {
@@ -3147,7 +2999,7 @@
     }
    ],
    "source": [
-    "A[keep_rows]\n"
+    "A[keep_rows]"
    ]
   },
   {
@@ -3197,7 +3049,7 @@
     "keep_cols = np.zeros(A.shape[1], bool)\n",
     "keep_cols[[0, 2, 3]] = True\n",
     "idx_bool = np.ix_(keep_rows, keep_cols)\n",
-    "A[idx_bool]\n"
+    "A[idx_bool]"
    ]
   },
   {
@@ -3218,8 +3070,7 @@
      "iopub.status.busy": "2023-07-26T05:29:17.885336Z",
      "iopub.status.idle": "2023-07-26T05:29:17.890989Z",
      "shell.execute_reply": "2023-07-26T05:29:17.890528Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -3236,15 +3087,7 @@
    ],
    "source": [
     "idx_mixed = np.ix_([1,3], keep_cols)\n",
-    "A[idx_mixed]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c679e7b",
-   "metadata": {},
-   "source": [
-    "    "
+    "A[idx_mixed]"
    ]
   },
   {
@@ -3253,7 +3096,7 @@
    "metadata": {},
    "source": [
     "For more details on indexing in `numpy`, readers are referred\n",
-    "to the `numpy` tutorial mentioned earlier.\n"
+    "to the `numpy` tutorial mentioned earlier."
    ]
   },
   {
@@ -3289,7 +3132,7 @@
     "as this notebook file, then we are all set. \n",
     "Otherwise, \n",
     "the command\n",
-    "`os.chdir()`  can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.) "
+    "`os.chdir()`  can be used to *change directory*. (You will need to call `import os` before calling `os.chdir()`.)"
    ]
   },
   {
@@ -3297,7 +3140,7 @@
    "id": "42760180",
    "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()`: "
+    "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()`:"
    ]
   },
   {
@@ -3521,7 +3364,7 @@
    "source": [
     "import pandas as pd\n",
     "Auto = pd.read_csv('Auto.csv')\n",
-    "Auto\n"
+    "Auto"
    ]
   },
   {
@@ -3542,12 +3385,11 @@
      "iopub.status.busy": "2023-07-26T05:29:18.129036Z",
      "iopub.status.idle": "2023-07-26T05:29:18.133861Z",
      "shell.execute_reply": "2023-07-26T05:29:18.133327Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [],
    "source": [
-    "Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n"
+    "Auto = pd.read_csv('Auto.data', delim_whitespace=True)"
    ]
   },
   {
@@ -3557,8 +3399,7 @@
    "source": [
     " Both `Auto.csv` and `Auto.data` are simply text\n",
     "files. Before loading data into `Python`, it is a good idea to view it using\n",
-    "a text editor or other software, such as Microsoft Excel.\n",
-    "\n"
+    "a text editor or other software, such as Microsoft Excel."
    ]
   },
   {
@@ -3566,7 +3407,7 @@
    "id": "1cae3ad2",
    "metadata": {},
    "source": [
-    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: "
+    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`:"
    ]
   },
   {
@@ -3579,8 +3420,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.136464Z",
      "iopub.status.idle": "2023-07-26T05:29:18.140452Z",
      "shell.execute_reply": "2023-07-26T05:29:18.139936Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -3606,7 +3446,7 @@
     }
    ],
    "source": [
-    "Auto['horsepower']\n"
+    "Auto['horsepower']"
    ]
   },
   {
@@ -3630,8 +3470,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.142803Z",
      "iopub.status.idle": "2023-07-26T05:29:18.146461Z",
      "shell.execute_reply": "2023-07-26T05:29:18.145934Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -3659,7 +3498,7 @@
     }
    ],
    "source": [
-    "np.unique(Auto['horsepower'])\n"
+    "np.unique(Auto['horsepower'])"
    ]
   },
   {
@@ -3667,8 +3506,7 @@
    "id": "c35e48dc",
    "metadata": {},
    "source": [
-    "We see the culprit is the value `?`, which is being used to encode missing values.\n",
-    "\n"
+    "We see the culprit is the value `?`, which is being used to encode missing values."
    ]
   },
   {
@@ -3691,8 +3529,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.148875Z",
      "iopub.status.idle": "2023-07-26T05:29:18.153953Z",
      "shell.execute_reply": "2023-07-26T05:29:18.153625Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -3710,7 +3547,7 @@
     "Auto = pd.read_csv('Auto.data',\n",
     "                   na_values=['?'],\n",
     "                   delim_whitespace=True)\n",
-    "Auto['horsepower'].sum()\n"
+    "Auto['horsepower'].sum()"
    ]
   },
   {
@@ -3747,7 +3584,7 @@
     }
    ],
    "source": [
-    "Auto.shape\n"
+    "Auto.shape"
    ]
   },
   {
@@ -3771,8 +3608,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.161286Z",
      "iopub.status.idle": "2023-07-26T05:29:18.165230Z",
      "shell.execute_reply": "2023-07-26T05:29:18.164876Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -3788,7 +3624,7 @@
    ],
    "source": [
     "Auto_new = Auto.dropna()\n",
-    "Auto_new.shape\n"
+    "Auto_new.shape"
    ]
   },
   {
@@ -3811,8 +3647,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.167156Z",
      "iopub.status.idle": "2023-07-26T05:29:18.171499Z",
      "shell.execute_reply": "2023-07-26T05:29:18.171112Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -3830,7 +3665,7 @@
    ],
    "source": [
     "Auto = Auto_new # overwrite the previous value\n",
-    "Auto.columns\n"
+    "Auto.columns"
    ]
   },
   {
@@ -3855,8 +3690,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.173779Z",
      "iopub.status.idle": "2023-07-26T05:29:18.180116Z",
      "shell.execute_reply": "2023-07-26T05:29:18.179681Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -3950,7 +3784,7 @@
     }
    ],
    "source": [
-    "Auto[:3]\n"
+    "Auto[:3]"
    ]
   },
   {
@@ -3971,8 +3805,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.183157Z",
      "iopub.status.idle": "2023-07-26T05:29:18.201363Z",
      "shell.execute_reply": "2023-07-26T05:29:18.200939Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -4837,7 +4670,7 @@
    ],
    "source": [
     "idx_80 = Auto['year'] > 80\n",
-    "Auto[idx_80]\n"
+    "Auto[idx_80]"
    ]
   },
   {
@@ -4845,7 +4678,7 @@
    "id": "27eb1780",
    "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*. "
+    "However, if we pass  in a list of strings to the `[]` method, then we obtain a data frame containing the corresponding set of *columns*."
    ]
   },
   {
@@ -4858,8 +4691,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.203352Z",
      "iopub.status.idle": "2023-07-26T05:29:18.209504Z",
      "shell.execute_reply": "2023-07-26T05:29:18.209134Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -4971,7 +4803,7 @@
     }
    ],
    "source": [
-    "Auto[['mpg', 'horsepower']]\n"
+    "Auto[['mpg', 'horsepower']]"
    ]
   },
   {
@@ -4993,8 +4825,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.211814Z",
      "iopub.status.idle": "2023-07-26T05:29:18.215209Z",
      "shell.execute_reply": "2023-07-26T05:29:18.214581Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -5012,7 +4843,7 @@
     }
    ],
    "source": [
-    "Auto.index\n"
+    "Auto.index"
    ]
   },
   {
@@ -5021,7 +4852,7 @@
    "metadata": {},
    "source": [
     "We can use the\n",
-    "`set_index()` method to re-name the rows using the contents of `Auto['name']`. "
+    "`set_index()` method to re-name the rows using the contents of `Auto['name']`."
    ]
   },
   {
@@ -5245,7 +5076,7 @@
    ],
    "source": [
     "Auto_re = Auto.set_index('name')\n",
-    "Auto_re\n"
+    "Auto_re"
    ]
   },
   {
@@ -5258,8 +5089,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.227297Z",
      "iopub.status.idle": "2023-07-26T05:29:18.230280Z",
      "shell.execute_reply": "2023-07-26T05:29:18.229861Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -5276,7 +5106,7 @@
     }
    ],
    "source": [
-    "Auto_re.columns\n"
+    "Auto_re.columns"
    ]
   },
   {
@@ -5301,8 +5131,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.232342Z",
      "iopub.status.idle": "2023-07-26T05:29:18.238242Z",
      "shell.execute_reply": "2023-07-26T05:29:18.237748Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -5393,7 +5222,7 @@
    ],
    "source": [
     "rows = ['amc rebel sst', 'ford torino']\n",
-    "Auto_re.loc[rows]\n"
+    "Auto_re.loc[rows]"
    ]
   },
   {
@@ -5414,8 +5243,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.240293Z",
      "iopub.status.idle": "2023-07-26T05:29:18.245745Z",
      "shell.execute_reply": "2023-07-26T05:29:18.245311Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -5505,7 +5333,7 @@
     }
    ],
    "source": [
-    "Auto_re.iloc[[3,4]]\n"
+    "Auto_re.iloc[[3,4]]"
    ]
   },
   {
@@ -5526,8 +5354,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.247834Z",
      "iopub.status.idle": "2023-07-26T05:29:18.254533Z",
      "shell.execute_reply": "2023-07-26T05:29:18.254012Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -5658,7 +5485,7 @@
     }
    ],
    "source": [
-    "Auto_re.iloc[:,[0,2,3]]\n"
+    "Auto_re.iloc[:,[0,2,3]]"
    ]
   },
   {
@@ -5680,8 +5507,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.256754Z",
      "iopub.status.idle": "2023-07-26T05:29:18.261662Z",
      "shell.execute_reply": "2023-07-26T05:29:18.261232Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -5746,7 +5572,7 @@
     }
    ],
    "source": [
-    "Auto_re.iloc[[3,4],[0,2,3]]\n"
+    "Auto_re.iloc[[3,4],[0,2,3]]"
    ]
   },
   {
@@ -5767,8 +5593,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.263676Z",
      "iopub.status.idle": "2023-07-26T05:29:18.268126Z",
      "shell.execute_reply": "2023-07-26T05:29:18.267724Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -5835,7 +5660,7 @@
     }
    ],
    "source": [
-    "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]\n"
+    "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]"
    ]
   },
   {
@@ -5860,8 +5685,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.270149Z",
      "iopub.status.idle": "2023-07-26T05:29:18.277370Z",
      "shell.execute_reply": "2023-07-26T05:29:18.276917Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -6259,7 +6083,7 @@
    ],
    "source": [
     "idx_80 = Auto_re['year'] > 80\n",
-    "Auto_re.loc[idx_80, ['weight', 'origin']]\n"
+    "Auto_re.loc[idx_80, ['weight', 'origin']]"
    ]
   },
   {
@@ -6267,7 +6091,7 @@
    "id": "d3f85975",
    "metadata": {},
    "source": [
-    "To do this more concisely, we can use an anonymous function called a `lambda`: "
+    "To do this more concisely, we can use an anonymous function called a `lambda`:"
    ]
   },
   {
@@ -6280,8 +6104,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.279296Z",
      "iopub.status.idle": "2023-07-26T05:29:18.287423Z",
      "shell.execute_reply": "2023-07-26T05:29:18.286943Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -6678,7 +6501,7 @@
     }
    ],
    "source": [
-    "Auto_re.loc[lambda df: df['year'] > 80, ['weight', 'origin']]\n"
+    "Auto_re.loc[lambda df: df['year'] > 80, ['weight', 'origin']]"
    ]
   },
   {
@@ -6704,8 +6527,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.289661Z",
      "iopub.status.idle": "2023-07-26T05:29:18.296360Z",
      "shell.execute_reply": "2023-07-26T05:29:18.295846Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -6960,7 +6782,7 @@
    "source": [
     "Auto_re.loc[lambda df: (df['year'] > 80) & (df['mpg'] > 30),\n",
     "            ['weight', 'origin']\n",
-    "           ]\n"
+    "           ]"
    ]
   },
   {
@@ -6984,8 +6806,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.298779Z",
      "iopub.status.idle": "2023-07-26T05:29:18.306530Z",
      "shell.execute_reply": "2023-07-26T05:29:18.305978Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7344,7 +7165,7 @@
     "                       & (df.index.str.contains('ford')\n",
     "                       | df.index.str.contains('datsun')),\n",
     "            ['weight', 'origin']\n",
-    "           ]\n"
+    "           ]"
    ]
   },
   {
@@ -7375,8 +7196,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.308549Z",
      "iopub.status.idle": "2023-07-26T05:29:18.311058Z",
      "shell.execute_reply": "2023-07-26T05:29:18.310729Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7391,7 +7211,7 @@
     "total = 0\n",
     "for value in [3,2,19]:\n",
     "    total += value\n",
-    "print('Total is: {0}'.format(total))\n"
+    "print('Total is: {0}'.format(total))"
    ]
   },
   {
@@ -7419,8 +7239,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.313627Z",
      "iopub.status.idle": "2023-07-26T05:29:18.316245Z",
      "shell.execute_reply": "2023-07-26T05:29:18.315821Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7487,7 +7306,7 @@
     "for value, weight in zip([2,3,19],\n",
     "                         [0.2,0.3,0.5]):\n",
     "    total += weight * value\n",
-    "print('Weighted average is: {0}'.format(total))\n"
+    "print('Weighted average is: {0}'.format(total))"
    ]
   },
   {
@@ -7526,8 +7345,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.323988Z",
      "iopub.status.idle": "2023-07-26T05:29:18.330121Z",
      "shell.execute_reply": "2023-07-26T05:29:18.329787Z"
-    },
-    "lines_to_next_cell": 2
+    }
    },
    "outputs": [
     {
@@ -7609,7 +7427,7 @@
     "                             'pickle',\n",
     "                             'snack',\n",
     "                             'popcorn'])\n",
-    "D[:3]\n"
+    "D[:3]"
    ]
   },
   {
@@ -7622,8 +7440,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.331972Z",
      "iopub.status.idle": "2023-07-26T05:29:18.335469Z",
      "shell.execute_reply": "2023-07-26T05:29:18.335131Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7642,7 +7459,7 @@
     "for col in D.columns:\n",
     "    template = 'Column \"{0}\" has {1:.2%} missing values'\n",
     "    print(template.format(col,\n",
-    "          np.isnan(D[col]).mean()))\n"
+    "          np.isnan(D[col]).mean()))"
    ]
   },
   {
@@ -7679,8 +7496,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.338751Z",
      "iopub.status.idle": "2023-07-26T05:29:18.449864Z",
      "shell.execute_reply": "2023-07-26T05:29:18.449132Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7728,8 +7544,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.452267Z",
      "iopub.status.idle": "2023-07-26T05:29:18.556826Z",
      "shell.execute_reply": "2023-07-26T05:29:18.556309Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7745,7 +7560,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');\n"
+    "ax.plot(Auto['horsepower'], Auto['mpg'], 'o');"
    ]
   },
   {
@@ -7757,7 +7572,7 @@
     "Using this method,\n",
     "the variables  can be accessed by name.\n",
     "The plot methods of a data frame return a familiar object:\n",
-    "an axes. We can use it to update the plot as we did previously: "
+    "an axes. We can use it to update the plot as we did previously:"
    ]
   },
   {
@@ -7770,8 +7585,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.559034Z",
      "iopub.status.idle": "2023-07-26T05:29:18.680294Z",
      "shell.execute_reply": "2023-07-26T05:29:18.679833Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7875,7 +7689,7 @@
    ],
    "source": [
     "fig, axes = subplots(ncols=3, figsize=(15, 5))\n",
-    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);\n"
+    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);"
    ]
   },
   {
@@ -7883,7 +7697,7 @@
    "id": "95b0f3de",
    "metadata": {},
    "source": [
-    "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`. "
+    "Note also that the columns of a data frame can be accessed as attributes: try typing in `Auto.horsepower`."
    ]
   },
   {
@@ -7907,8 +7721,7 @@
      "iopub.status.busy": "2023-07-26T05:29:18.937680Z",
      "iopub.status.idle": "2023-07-26T05:29:18.941712Z",
      "shell.execute_reply": "2023-07-26T05:29:18.941344Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -7924,7 +7737,7 @@
    ],
    "source": [
     "Auto.cylinders = pd.Series(Auto.cylinders, dtype='category')\n",
-    "Auto.cylinders.dtype\n"
+    "Auto.cylinders.dtype"
    ]
   },
   {
@@ -7962,7 +7775,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.boxplot('mpg', by='cylinders', ax=ax);\n"
+    "Auto.boxplot('mpg', by='cylinders', ax=ax);"
    ]
   },
   {
@@ -7983,8 +7796,7 @@
      "iopub.status.busy": "2023-07-26T05:29:19.071061Z",
      "iopub.status.idle": "2023-07-26T05:29:19.195022Z",
      "shell.execute_reply": "2023-07-26T05:29:19.194542Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -8000,7 +7812,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.hist('mpg', ax=ax);\n"
+    "Auto.hist('mpg', ax=ax);"
    ]
   },
   {
@@ -8021,8 +7833,7 @@
      "iopub.status.busy": "2023-07-26T05:29:19.197276Z",
      "iopub.status.idle": "2023-07-26T05:29:19.318988Z",
      "shell.execute_reply": "2023-07-26T05:29:19.318622Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -8038,7 +7849,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.hist('mpg', color='red', bins=12, ax=ax);\n"
+    "Auto.hist('mpg', color='red', bins=12, ax=ax);"
    ]
   },
   {
@@ -8063,8 +7874,7 @@
      "iopub.status.busy": "2023-07-26T05:29:19.321240Z",
      "iopub.status.idle": "2023-07-26T05:29:20.519159Z",
      "shell.execute_reply": "2023-07-26T05:29:20.518700Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -8079,7 +7889,7 @@
     }
    ],
    "source": [
-    "pd.plotting.scatter_matrix(Auto);\n"
+    "pd.plotting.scatter_matrix(Auto);"
    ]
   },
   {
@@ -8101,8 +7911,7 @@
      "iopub.status.busy": "2023-07-26T05:29:20.521429Z",
      "iopub.status.idle": "2023-07-26T05:29:20.804202Z",
      "shell.execute_reply": "2023-07-26T05:29:20.803825Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -8119,7 +7928,7 @@
    "source": [
     "pd.plotting.scatter_matrix(Auto[['mpg',\n",
     "                                 'displacement',\n",
-    "                                 'weight']]);\n"
+    "                                 'weight']]);"
    ]
   },
   {
@@ -8140,8 +7949,7 @@
      "iopub.status.busy": "2023-07-26T05:29:20.809455Z",
      "iopub.status.idle": "2023-07-26T05:29:20.816572Z",
      "shell.execute_reply": "2023-07-26T05:29:20.816157Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -8232,7 +8040,7 @@
     }
    ],
    "source": [
-    "Auto[['mpg', 'weight']].describe()\n"
+    "Auto[['mpg', 'weight']].describe()"
    ]
   },
   {
@@ -8253,8 +8061,7 @@
      "iopub.status.busy": "2023-07-26T05:29:20.818991Z",
      "iopub.status.idle": "2023-07-26T05:29:20.824216Z",
      "shell.execute_reply": "2023-07-26T05:29:20.823857Z"
-    },
-    "lines_to_next_cell": 0
+    }
    },
    "outputs": [
     {
@@ -8278,7 +8085,7 @@
    ],
    "source": [
     "Auto['cylinders'].describe()\n",
-    "Auto['mpg'].describe()\n"
+    "Auto['mpg'].describe()"
    ]
   },
   {
@@ -8286,18 +8093,15 @@
    "id": "22ecf5c3",
    "metadata": {},
    "source": [
-    "To exit `Jupyter`,  select `File / Close and Halt`.\n",
-    "\n",
-    "\n",
-    "\n"
+    "To exit `Jupyter`,  select `File / Close and Halt`."
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Ch3-linreg-lab.ipynb b/Ch3-linreg-lab.ipynb
index 72e3242..be6a6c6 100644
--- a/Ch3-linreg-lab.ipynb
+++ b/Ch3-linreg-lab.ipynb
@@ -5,9 +5,7 @@
    "id": "82bce88a",
    "metadata": {},
    "source": [
-    "\n",
-    "# Chapter 3\n",
-    "\n"
+    "# Chapter 3"
    ]
   },
   {
@@ -28,18 +26,17 @@
    "id": "ca5277a6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:48.562599Z",
-     "iopub.status.busy": "2023-07-26T05:16:48.562472Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.062186Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.061609Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:25:32.624111Z",
+     "iopub.status.busy": "2023-07-26T19:25:32.623997Z",
+     "iopub.status.idle": "2023-07-26T19:25:33.256792Z",
+     "shell.execute_reply": "2023-07-26T19:25:33.256463Z"
+    }
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from matplotlib.pyplot import subplots\n"
+    "from matplotlib.pyplot import subplots"
    ]
   },
   {
@@ -61,16 +58,15 @@
    "id": "675f24e6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.064676Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.064483Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.739307Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.738853Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:33.258895Z",
+     "iopub.status.busy": "2023-07-26T19:25:33.258739Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.130442Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.130023Z"
+    }
    },
    "outputs": [],
    "source": [
-    "import statsmodels.api as sm\n"
+    "import statsmodels.api as sm"
    ]
   },
   {
@@ -94,17 +90,17 @@
    "id": "a0ee23c2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.741758Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.741609Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.744969Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.744551Z"
+     "iopub.execute_input": "2023-07-26T19:25:34.132813Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.132611Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.136142Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.135716Z"
     }
    },
    "outputs": [],
    "source": [
     "from statsmodels.stats.outliers_influence \\\n",
     "     import variance_inflation_factor as VIF\n",
-    "from statsmodels.stats.anova import anova_lm\n"
+    "from statsmodels.stats.anova import anova_lm"
    ]
   },
   {
@@ -125,10 +121,10 @@
    "id": "b35eb887",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.747166Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.746991Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.867818Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.867356Z"
+     "iopub.execute_input": "2023-07-26T19:25:34.138142Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.138002Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.430685Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.430139Z"
     }
    },
    "outputs": [],
@@ -136,7 +132,7 @@
     "from ISLP import load_data\n",
     "from ISLP.models import (ModelSpec as MS,\n",
     "                         summarize,\n",
-    "                         poly)\n"
+    "                         poly)"
    ]
   },
   {
@@ -157,12 +153,11 @@
    "id": "961908f7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.870378Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.870231Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.874355Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.873851Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.433283Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.432983Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.437209Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.436904Z"
+    }
    },
    "outputs": [
     {
@@ -213,7 +208,7 @@
     }
    ],
    "source": [
-    "dir()\n"
+    "dir()"
    ]
   },
   {
@@ -238,12 +233,11 @@
    "id": "662caa15",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.876620Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.876476Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.880485Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.880074Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.438902Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.438784Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.442144Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.441750Z"
+    }
    },
    "outputs": [
     {
@@ -264,6 +258,7 @@
        " '__array_wrap__',\n",
        " '__bool__',\n",
        " '__class__',\n",
+       " '__class_getitem__',\n",
        " '__complex__',\n",
        " '__contains__',\n",
        " '__copy__',\n",
@@ -272,6 +267,8 @@
        " '__delitem__',\n",
        " '__dir__',\n",
        " '__divmod__',\n",
+       " '__dlpack__',\n",
+       " '__dlpack_device__',\n",
        " '__doc__',\n",
        " '__eq__',\n",
        " '__float__',\n",
@@ -280,6 +277,7 @@
        " '__ge__',\n",
        " '__getattribute__',\n",
        " '__getitem__',\n",
+       " '__getstate__',\n",
        " '__gt__',\n",
        " '__hash__',\n",
        " '__iadd__',\n",
@@ -420,7 +418,7 @@
    ],
    "source": [
     "A = np.array([3,5,11])\n",
-    "dir(A)\n"
+    "dir(A)"
    ]
   },
   {
@@ -438,12 +436,11 @@
    "id": "ebb7d126",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.882618Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.882477Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.885324Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.884886Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.443858Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.443746Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.446152Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.445877Z"
+    }
    },
    "outputs": [
     {
@@ -458,15 +455,7 @@
     }
    ],
    "source": [
-    "A.sum()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b9db985",
-   "metadata": {},
-   "source": [
-    "    "
+    "A.sum()"
    ]
   },
   {
@@ -496,10 +485,10 @@
    "id": "1ea46cee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.887855Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.887580Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.892800Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.892442Z"
+     "iopub.execute_input": "2023-07-26T19:25:34.447642Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.447543Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.453065Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.452749Z"
     }
    },
    "outputs": [
@@ -518,7 +507,7 @@
    ],
    "source": [
     "Boston = load_data(\"Boston\")\n",
-    "Boston.columns\n"
+    "Boston.columns"
    ]
   },
   {
@@ -531,7 +520,7 @@
     "We start by using the `sm.OLS()`  function to fit a\n",
     "simple linear regression model.  Our response will be\n",
     " `medv`  and  `lstat`  will be the single predictor.\n",
-    "For this model, we can create the model matrix by hand.\n"
+    "For this model, we can create the model matrix by hand."
    ]
   },
   {
@@ -540,10 +529,10 @@
    "id": "26c0ba88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.895108Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.894938Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.901385Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.900996Z"
+     "iopub.execute_input": "2023-07-26T19:25:34.454894Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.454762Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.461258Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.460900Z"
     }
    },
    "outputs": [
@@ -613,7 +602,7 @@
    "source": [
     "X = pd.DataFrame({'intercept': np.ones(Boston.shape[0]),\n",
     "                  'lstat': Boston['lstat']})\n",
-    "X[:4]\n"
+    "X[:4]"
    ]
   },
   {
@@ -630,18 +619,17 @@
    "id": "d4dd511b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.903600Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.903426Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.906235Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.905900Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.463337Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.463168Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.466052Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.465747Z"
+    }
    },
    "outputs": [],
    "source": [
     "y = Boston['medv']\n",
     "model = sm.OLS(y, X)\n",
-    "results = model.fit()\n"
+    "results = model.fit()"
    ]
   },
   {
@@ -665,12 +653,11 @@
    "id": "eef9f8e3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.908149Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.908028Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.973872Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.973443Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:25:34.467850Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.467730Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.537404Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.536985Z"
+    }
    },
    "outputs": [
     {
@@ -731,7 +718,7 @@
     }
    ],
    "source": [
-    "summarize(results)\n"
+    "summarize(results)"
    ]
   },
   {
@@ -767,7 +754,7 @@
     "initial computations on it, as specified in the transform object.\n",
     "For example, it may compute means and standard deviations for centering and scaling.\n",
     "The `transform()` \n",
-    "method applies the fitted transformation to the array of data, and produces the model matrix.\n"
+    "method applies the fitted transformation to the array of data, and produces the model matrix."
    ]
   },
   {
@@ -776,12 +763,11 @@
    "id": "557170d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.976562Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.976352Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.982733Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.982315Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.539321Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.539151Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.545291Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.544760Z"
+    }
    },
    "outputs": [
     {
@@ -871,12 +857,11 @@
    "id": "b83ec097",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.985396Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.985228Z",
-     "iopub.status.idle": "2023-07-26T05:16:49.992390Z",
-     "shell.execute_reply": "2023-07-26T05:16:49.991020Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.547368Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.547197Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.552566Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.552239Z"
+    }
    },
    "outputs": [
     {
@@ -975,10 +960,10 @@
    "id": "d4dce5f6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:49.995458Z",
-     "iopub.status.busy": "2023-07-26T05:16:49.995307Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.003927Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.003569Z"
+     "iopub.execute_input": "2023-07-26T19:25:34.555115Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.554960Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.566180Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.565715Z"
     }
    },
    "outputs": [
@@ -997,10 +982,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   601.6</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 25 Jul 2023</td> <th>  Prob (F-statistic):</th> <td>5.08e-88</td>\n",
+       "  <th>Date:</th>             <td>Wed, 26 Jul 2023</td> <th>  Prob (F-statistic):</th> <td>5.08e-88</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>22:16:49</td>     <th>  Log-Likelihood:    </th> <td> -1641.5</td>\n",
+       "  <th>Time:</th>                 <td>15:25:34</td>     <th>  Log-Likelihood:    </th> <td> -1641.5</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3287.</td>\n",
@@ -1041,6 +1026,41 @@
        "</tr>\n",
        "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\toprule\n",
+       "\\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:}             & Wed, 26 Jul 2023 & \\textbf{  Prob (F-statistic):} &  5.08e-88   \\\\\n",
+       "\\textbf{Time:}             &     15:25:34     & \\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",
+       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{intercept} &      34.5538  &        0.563     &    61.415  &         0.000        &       33.448    &       35.659     \\\\\n",
+       "\\textbf{lstat}     &      -0.9500  &        0.039     &   -24.528  &         0.000        &       -1.026    &       -0.874     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\begin{tabular}{lclc}\n",
+       "\\textbf{Omnibus:}       & 137.043 & \\textbf{  Durbin-Watson:     } &    0.892  \\\\\n",
+       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &  291.373  \\\\\n",
+       "\\textbf{Skew:}          &   1.453 & \\textbf{  Prob(JB):          } & 5.36e-64  \\\\\n",
+       "\\textbf{Kurtosis:}      &   5.319 & \\textbf{  Cond. No.          } &     29.7  \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "%\\caption{OLS Regression Results}\n",
+       "\\end{center}\n",
+       "\n",
+       "Notes: \\newline\n",
+       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
@@ -1049,8 +1069,8 @@
        "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:                Tue, 25 Jul 2023   Prob (F-statistic):           5.08e-88\n",
-       "Time:                        22:16:49   Log-Likelihood:                -1641.5\n",
+       "Date:                Wed, 26 Jul 2023   Prob (F-statistic):           5.08e-88\n",
+       "Time:                        15:25:34   Log-Likelihood:                -1641.5\n",
        "No. Observations:                 506   AIC:                             3287.\n",
        "Df Residuals:                     504   BIC:                             3295.\n",
        "Df Model:                           1                                         \n",
@@ -1078,7 +1098,7 @@
     }
    ],
    "source": [
-    "results.summary()\n"
+    "results.summary()"
    ]
   },
   {
@@ -1096,12 +1116,11 @@
    "id": "a0edf555",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.006397Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.006253Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.009669Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.009037Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:25:34.568168Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.568039Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.571039Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.570733Z"
+    }
    },
    "outputs": [
     {
@@ -1118,7 +1137,7 @@
     }
    ],
    "source": [
-    "results.params\n"
+    "results.params"
    ]
   },
   {
@@ -1139,10 +1158,10 @@
    "id": "fdc5a3f3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.012044Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.011931Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.016842Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.016427Z"
+     "iopub.execute_input": "2023-07-26T19:25:34.572640Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.572537Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.576490Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.576202Z"
     }
    },
    "outputs": [
@@ -1206,7 +1225,7 @@
    "source": [
     "new_df = pd.DataFrame({'lstat':[5, 10, 15]})\n",
     "newX = design.transform(new_df)\n",
-    "newX\n"
+    "newX"
    ]
   },
   {
@@ -1223,12 +1242,11 @@
    "id": "2c6acbf0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.019127Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.018964Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.022350Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.021918Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.578008Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.577908Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.581020Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.580700Z"
+    }
    },
    "outputs": [
     {
@@ -1244,7 +1262,7 @@
    ],
    "source": [
     "new_predictions = results.get_prediction(newX);\n",
-    "new_predictions.predicted_mean\n"
+    "new_predictions.predicted_mean"
    ]
   },
   {
@@ -1261,12 +1279,11 @@
    "id": "c472ef33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.024574Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.024433Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.027837Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.027439Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.582654Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.582544Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.585405Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.585114Z"
+    }
    },
    "outputs": [
     {
@@ -1283,7 +1300,7 @@
     }
    ],
    "source": [
-    "new_predictions.conf_int(alpha=0.05)\n"
+    "new_predictions.conf_int(alpha=0.05)"
    ]
   },
   {
@@ -1300,12 +1317,11 @@
    "id": "3e2ffc7a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.029978Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.029838Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.032847Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.032491Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.587009Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.586896Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.589601Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.589306Z"
+    }
    },
    "outputs": [
     {
@@ -1322,7 +1338,7 @@
     }
    ],
    "source": [
-    "new_predictions.conf_int(obs=True, alpha=0.05)\n"
+    "new_predictions.conf_int(obs=True, alpha=0.05)"
    ]
   },
   {
@@ -1360,12 +1376,11 @@
    "id": "4e56a1d3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.035185Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.035076Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.037782Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.037389Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.591430Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.591295Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.593482Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.593195Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -1373,7 +1388,7 @@
     "    \"Add a line with slope m and intercept b to ax\"\n",
     "    xlim = ax.get_xlim()\n",
     "    ylim = [m * xlim[0] + b, m * xlim[1] + b]\n",
-    "    ax.plot(xlim, ylim)\n"
+    "    ax.plot(xlim, ylim)"
    ]
   },
   {
@@ -1394,12 +1409,11 @@
    "id": "7f43ffe7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.040033Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.039836Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.042447Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.042026Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.595153Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.595035Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.597180Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.596878Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -1407,7 +1421,7 @@
     "    \"Add a line with slope m and intercept b to ax\"\n",
     "    xlim = ax.get_xlim()\n",
     "    ylim = [m * xlim[0] + b, m * xlim[1] + b]\n",
-    "    ax.plot(xlim, ylim, *args, **kwargs)\n"
+    "    ax.plot(xlim, ylim, *args, **kwargs)"
    ]
   },
   {
@@ -1434,22 +1448,13 @@
    "id": "3f7b67c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.044790Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.044610Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.159032Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.158476Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.598798Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.598688Z",
+     "iopub.status.idle": "2023-07-26T19:25:34.877477Z",
+     "shell.execute_reply": "2023-07-26T19:25:34.876128Z"
+    }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
-      "  scatter = ax.scatter(\n"
-     ]
-    },
     {
      "data": {
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",
@@ -1467,7 +1472,7 @@
     "       results.params[0],\n",
     "       results.params[1],\n",
     "       'r--',\n",
-    "       linewidth=3)\n"
+    "       linewidth=3)"
    ]
   },
   {
@@ -1480,8 +1485,7 @@
     "an argument to make it of width 3.\n",
     "There is some evidence for non-linearity in the relationship between  `lstat`  and  `medv`. We will explore this issue later in this lab.\n",
     "\n",
-    "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays.\n",
-    "\n"
+    "As mentioned above, there is an existing function to add a line to a plot --- `ax.axline()` --- but knowing how to write such functions empowers us to create more expressive displays."
    ]
   },
   {
@@ -1506,12 +1510,11 @@
    "id": "b35a2fd3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.161898Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.161592Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.274404Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.273842Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:34.883302Z",
+     "iopub.status.busy": "2023-07-26T19:25:34.882776Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.016101Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.015775Z"
+    }
    },
    "outputs": [
     {
@@ -1530,7 +1533,7 @@
     "ax.scatter(results.fittedvalues, results.resid)\n",
     "ax.set_xlabel('Fitted value')\n",
     "ax.set_ylabel('Residual')\n",
-    "ax.axhline(0, c='k', ls='--');\n"
+    "ax.axhline(0, c='k', ls='--');"
    ]
   },
   {
@@ -1554,12 +1557,11 @@
    "id": "82673b80",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.276799Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.276637Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.384102Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.383325Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.017807Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.017687Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.110396Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.110033Z"
+    }
    },
    "outputs": [
     {
@@ -1589,7 +1591,7 @@
     "ax.scatter(np.arange(X.shape[0]), infl.hat_matrix_diag)\n",
     "ax.set_xlabel('Index')\n",
     "ax.set_ylabel('Leverage')\n",
-    "np.argmax(infl.hat_matrix_diag)\n"
+    "np.argmax(infl.hat_matrix_diag)"
    ]
   },
   {
@@ -1622,12 +1624,11 @@
    "id": "54596dc4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.387053Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.386874Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.400398Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.399938Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.112294Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.112163Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.122706Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.122385Z"
+    }
    },
    "outputs": [
     {
@@ -1720,10 +1721,10 @@
    "id": "75c78238",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.402974Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.402819Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.406561Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.406042Z"
+     "iopub.execute_input": "2023-07-26T19:25:35.124413Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.124297Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.126957Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.126656Z"
     }
    },
    "outputs": [
@@ -1742,7 +1743,7 @@
    ],
    "source": [
     "terms = Boston.columns.drop('medv')\n",
-    "terms\n"
+    "terms"
    ]
   },
   {
@@ -1760,10 +1761,10 @@
    "id": "f14b9e1a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.409092Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.408922Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.427019Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.426146Z"
+     "iopub.execute_input": "2023-07-26T19:25:35.128696Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.128578Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.142971Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.142711Z"
     }
    },
    "outputs": [
@@ -1916,7 +1917,7 @@
     "X = MS(terms).fit_transform(Boston)\n",
     "model = sm.OLS(y, X)\n",
     "results = model.fit()\n",
-    "summarize(results)\n"
+    "summarize(results)"
    ]
   },
   {
@@ -1936,10 +1937,10 @@
    "id": "0a2714b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.430858Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.430692Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.449487Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.448800Z"
+     "iopub.execute_input": "2023-07-26T19:25:35.144443Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.144358Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.157818Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.157538Z"
     }
    },
    "outputs": [
@@ -2084,7 +2085,7 @@
     "minus_age = Boston.columns.drop(['medv', 'age']) \n",
     "Xma = MS(minus_age).fit_transform(Boston)\n",
     "model1 = sm.OLS(y, Xma)\n",
-    "summarize(model1.fit())\n"
+    "summarize(model1.fit())"
    ]
   },
   {
@@ -2115,7 +2116,7 @@
     "lists of `Python` objects. The language also supports\n",
     "dictionary and *generator* comprehension, though these are\n",
     "beyond our scope here. Let's look at an example. We compute the VIF for each of the variables\n",
-    "in the model matrix `X`, using the function `variance_inflation_factor()`.\n"
+    "in the model matrix `X`, using the function `variance_inflation_factor()`."
    ]
   },
   {
@@ -2124,12 +2125,11 @@
    "id": "961c9128",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.454906Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.454582Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.464023Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.463522Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.159295Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.159206Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.165985Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.165716Z"
+    }
    },
    "outputs": [
     {
@@ -2235,7 +2235,7 @@
     "        for i in range(1, X.shape[1])]\n",
     "vif = pd.DataFrame({'vif':vals},\n",
     "                   index=X.columns[1:])\n",
-    "vif\n"
+    "vif"
    ]
   },
   {
@@ -2256,18 +2256,17 @@
    "id": "4886f9e9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.466225Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.466081Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.472696Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.472188Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.168001Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.167692Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.172979Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.172702Z"
+    }
    },
    "outputs": [],
    "source": [
     "vals = []\n",
     "for i in range(1, X.values.shape[1]):\n",
-    "    vals.append(VIF(X.values, i))\n"
+    "    vals.append(VIF(X.values, i))"
    ]
   },
   {
@@ -2289,12 +2288,11 @@
    "id": "b54d2da1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.475003Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.474857Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.488206Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.487746Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:25:35.174746Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.174650Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.185699Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.185415Z"
+    }
    },
    "outputs": [
     {
@@ -2375,7 +2373,7 @@
     "        'age',\n",
     "        ('lstat', 'age')]).fit_transform(Boston)\n",
     "model2 = sm.OLS(y, X)\n",
-    "summarize(model2.fit())\n"
+    "summarize(model2.fit())"
    ]
   },
   {
@@ -2397,12 +2395,11 @@
    "id": "1b71633a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.490521Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.490379Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.504489Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.504059Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.187478Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.187331Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.198747Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.198420Z"
+    }
    },
    "outputs": [
     {
@@ -2482,7 +2479,7 @@
     "X = MS([poly('lstat', degree=2), 'age']).fit_transform(Boston)\n",
     "model3 = sm.OLS(y, X)\n",
     "results3 = model3.fit()\n",
-    "summarize(results3)\n"
+    "summarize(results3)"
    ]
   },
   {
@@ -2515,12 +2512,11 @@
    "id": "6d30a306",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.506891Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.506725Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.513731Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.513303Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.200461Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.200335Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.205981Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.205669Z"
+    }
    },
    "outputs": [
     {
@@ -2587,7 +2583,7 @@
     }
    ],
    "source": [
-    "anova_lm(results1, results3)\n"
+    "anova_lm(results1, results3)"
    ]
   },
   {
@@ -2616,7 +2612,7 @@
     "The function `anova_lm()` can take more than two nested models\n",
     "as input, in which case it compares every successive pair of models.\n",
     "That also explains why their are `NaN`s in the first row above, since\n",
-    "there is no previous model with which to compare the first.\n"
+    "there is no previous model with which to compare the first."
    ]
   },
   {
@@ -2625,18 +2621,17 @@
    "id": "9a5ec13f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.516035Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.515894Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.640835Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.640080Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.207713Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.207599Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.333108Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.332719Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x286c2c520>"
+       "<matplotlib.lines.Line2D at 0x17fb60110>"
       ]
      },
      "execution_count": 34,
@@ -2659,7 +2654,7 @@
     "ax.scatter(results3.fittedvalues, results3.resid)\n",
     "ax.set_xlabel('Fitted value')\n",
     "ax.set_ylabel('Residual')\n",
-    "ax.axhline(0, c='k', ls='--')\n"
+    "ax.axhline(0, c='k', ls='--')"
    ]
   },
   {
@@ -2670,7 +2665,7 @@
     "We see that when the quadratic term is included in the model,\n",
     "there is little discernible pattern in the residuals.\n",
     "In order to create a cubic or higher-degree polynomial fit, we can simply change the degree argument\n",
-    "to `poly()`.\n"
+    "to `poly()`."
    ]
   },
   {
@@ -2691,12 +2686,11 @@
    "id": "09bbc0c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.643135Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.643008Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.649451Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.649047Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.335029Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.334905Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.340911Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.340638Z"
+    }
    },
    "outputs": [
     {
@@ -2714,7 +2708,7 @@
    ],
    "source": [
     "Carseats = load_data('Carseats')\n",
-    "Carseats.columns\n"
+    "Carseats.columns"
    ]
   },
   {
@@ -2742,12 +2736,11 @@
    "id": "2e1da1fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:50.651966Z",
-     "iopub.status.busy": "2023-07-26T05:16:50.651596Z",
-     "iopub.status.idle": "2023-07-26T05:16:50.678716Z",
-     "shell.execute_reply": "2023-07-26T05:16:50.678209Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:25:35.342743Z",
+     "iopub.status.busy": "2023-07-26T19:25:35.342451Z",
+     "iopub.status.idle": "2023-07-26T19:25:35.364483Z",
+     "shell.execute_reply": "2023-07-26T19:25:35.364162Z"
+    }
    },
    "outputs": [
     {
@@ -2910,7 +2903,7 @@
     "                   ('Price', 'Age')]\n",
     "X = MS(final).fit_transform(Carseats)\n",
     "model = sm.OLS(y, X)\n",
-    "summarize(model.fit())\n"
+    "summarize(model.fit())"
    ]
   },
   {
@@ -2929,16 +2922,15 @@
     "positive indicates that a good shelving location is associated with high sales (relative to a bad location).\n",
     "And `ShelveLoc[Medium]` has a smaller positive coefficient,\n",
     "indicating that a medium shelving location leads to higher sales than a bad\n",
-    "shelving location, but lower sales than a good shelving location.\n",
-    "\n"
+    "shelving location, but lower sales than a good shelving location."
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2950,7 +2942,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch4-classification-lab.ipynb b/Ch4-classification-lab.ipynb
index 707e376..c03e3f2 100644
--- a/Ch4-classification-lab.ipynb
+++ b/Ch4-classification-lab.ipynb
@@ -5,10 +5,7 @@
    "id": "e6cc127e",
    "metadata": {},
    "source": [
-    "\n",
-    "# Chapter 4\n",
-    "\n",
-    "\n"
+    "# Chapter 4"
    ]
   },
   {
@@ -16,7 +13,7 @@
    "id": "af3093e5",
    "metadata": {},
    "source": [
-    "# Lab: Logistic Regression, LDA, QDA, and KNN\n"
+    "# Lab: Logistic Regression, LDA, QDA, and KNN"
    ]
   },
   {
@@ -46,10 +43,10 @@
    "id": "71523a7f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:52.147230Z",
-     "iopub.status.busy": "2023-07-26T05:16:52.147074Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.319384Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.318779Z"
+     "iopub.execute_input": "2023-07-26T19:27:21.906136Z",
+     "iopub.status.busy": "2023-07-26T19:27:21.906046Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.156580Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.156151Z"
     }
    },
    "outputs": [],
@@ -77,12 +74,11 @@
    "id": "901ed7c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.322482Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.322239Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.343077Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.342596Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.158787Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.158544Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.177637Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.177316Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -95,7 +91,7 @@
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegression\n"
+    "from sklearn.linear_model import LogisticRegression"
    ]
   },
   {
@@ -112,12 +108,11 @@
    "id": "8b56ee0f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.345526Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.345383Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.358625Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.357838Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.179920Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.179782Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.194330Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.194019Z"
+    }
    },
    "outputs": [
     {
@@ -332,12 +327,11 @@
    "id": "62d49b47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.361002Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.360735Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.364261Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.363501Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.196394Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.196266Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.199079Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.198697Z"
+    }
    },
    "outputs": [
     {
@@ -376,12 +370,11 @@
    "id": "bc34e7b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.366253Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.366125Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.372926Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.372328Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.200540Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.200435Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.206001Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.205712Z"
+    }
    },
    "outputs": [
     {
@@ -536,7 +529,7 @@
     }
    ],
    "source": [
-    "Smarket.corr()\n"
+    "Smarket.corr(numeric_only=True)"
    ]
   },
   {
@@ -548,7 +541,7 @@
     "today’s return are close to zero.  The only substantial correlation is between  `Year`  and\n",
     " `Volume`. By plotting the data we see that  `Volume`\n",
     "is increasing over time. In other words, the average number of shares traded\n",
-    "daily increased from 2001 to 2005.\n"
+    "daily increased from 2001 to 2005."
    ]
   },
   {
@@ -557,12 +550,11 @@
    "id": "9f075144",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.376785Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.376551Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.491023Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.490615Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.207725Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.207590Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.327067Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.326359Z"
+    }
    },
    "outputs": [
     {
@@ -577,7 +569,7 @@
     }
    ],
    "source": [
-    "Smarket.plot(y='Volume');\n"
+    "Smarket.plot(y='Volume');"
    ]
   },
   {
@@ -604,12 +596,11 @@
    "id": "2ab15aa3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.493500Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.493302Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.566736Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.566181Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.332167Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.331902Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.423774Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.422968Z"
+    }
    },
    "outputs": [
     {
@@ -718,7 +709,7 @@
     "             X,\n",
     "             family=sm.families.Binomial())\n",
     "results = glm.fit()\n",
-    "summarize(results)\n"
+    "summarize(results)"
    ]
   },
   {
@@ -744,12 +735,11 @@
    "id": "0faa6a6c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.569154Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.568975Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.572426Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.572078Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.428313Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.427906Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.440484Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.439867Z"
+    }
    },
    "outputs": [
     {
@@ -771,7 +761,7 @@
     }
    ],
    "source": [
-    "results.params\n"
+    "results.params"
    ]
   },
   {
@@ -789,10 +779,10 @@
    "id": "108aff98",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.574612Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.574492Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.578308Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.577846Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.444693Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.444340Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.459304Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.458514Z"
     }
    },
    "outputs": [
@@ -815,7 +805,7 @@
     }
    ],
    "source": [
-    "results.pvalues\n"
+    "results.pvalues"
    ]
   },
   {
@@ -840,12 +830,11 @@
    "id": "54b4b96c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.580578Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.580429Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.583750Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.583333Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.464161Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.463923Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.473037Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.472236Z"
+    }
    },
    "outputs": [
     {
@@ -862,7 +851,7 @@
    ],
    "source": [
     "probs = results.predict()\n",
-    "probs[:10]\n"
+    "probs[:10]"
    ]
   },
   {
@@ -884,17 +873,16 @@
    "id": "46c2887c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.586085Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.585953Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.588706Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.588288Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.478266Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.477812Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.485616Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.484880Z"
+    }
    },
    "outputs": [],
    "source": [
     "labels = np.array(['Down']*1250)\n",
-    "labels[probs>0.5] = \"Up\"\n"
+    "labels[probs>0.5] = \"Up\""
    ]
   },
   {
@@ -917,10 +905,10 @@
    "id": "0f816c4d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.590957Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.590766Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.598268Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.597798Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.492550Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.492272Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.501794Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.501509Z"
     }
    },
    "outputs": [
@@ -982,7 +970,7 @@
     }
    ],
    "source": [
-    "confusion_table(labels, Smarket.Direction)\n"
+    "confusion_table(labels, Smarket.Direction)"
    ]
   },
   {
@@ -997,7 +985,7 @@
     "total of 507 + 145 = 652 correct predictions. The `np.mean()`\n",
     "function can be used to compute the fraction of days for which the\n",
     "prediction was correct. In this case, logistic regression correctly\n",
-    "predicted the movement of the market 52.2% of the time.\n"
+    "predicted the movement of the market 52.2% of the time."
    ]
   },
   {
@@ -1006,12 +994,11 @@
    "id": "23668517",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.600706Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.600511Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.603872Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.603421Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.503365Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.503272Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.505942Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.505698Z"
+    }
    },
    "outputs": [
     {
@@ -1026,15 +1013,7 @@
     }
    ],
    "source": [
-    "(507+145)/1250, np.mean(labels == Smarket.Direction)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b1e8bf1",
-   "metadata": {},
-   "source": [
-    "\n"
+    "(507+145)/1250, np.mean(labels == Smarket.Direction)"
    ]
   },
   {
@@ -1069,12 +1048,11 @@
    "id": "86a02d7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.606055Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.605931Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.609917Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.609576Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.507395Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.507290Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.510226Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.509994Z"
+    }
    },
    "outputs": [
     {
@@ -1092,7 +1070,7 @@
     "train = (Smarket.Year < 2005)\n",
     "Smarket_train = Smarket.loc[train]\n",
     "Smarket_test = Smarket.loc[~train]\n",
-    "Smarket_test.shape\n"
+    "Smarket_test.shape"
    ]
   },
   {
@@ -1135,10 +1113,10 @@
    "id": "f23b1c7b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.612185Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.612009Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.618057Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.617645Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.511679Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.511581Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.515967Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.515729Z"
     }
    },
    "outputs": [],
@@ -1149,7 +1127,7 @@
     "                   X_train,\n",
     "                   family=sm.families.Binomial())\n",
     "results = glm_train.fit()\n",
-    "probs = results.predict(exog=X_test)\n"
+    "probs = results.predict(exog=X_test)"
    ]
   },
   {
@@ -1172,17 +1150,16 @@
    "id": "82f849b9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.620635Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.620266Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.623107Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.622634Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.517433Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.517344Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.519357Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.519109Z"
+    }
    },
    "outputs": [],
    "source": [
     "D = Smarket.Direction\n",
-    "L_train, L_test = D.loc[train], D.loc[~train]\n"
+    "L_train, L_test = D.loc[train], D.loc[~train]"
    ]
   },
   {
@@ -1201,12 +1178,11 @@
    "id": "e216583a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.625601Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.625081Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.631930Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.631398Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.520788Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.520699Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.525000Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.524716Z"
+    }
    },
    "outputs": [
     {
@@ -1269,7 +1245,7 @@
    "source": [
     "labels = np.array(['Down']*252)\n",
     "labels[probs>0.5] = 'Up'\n",
-    "confusion_table(labels, L_test)\n"
+    "confusion_table(labels, L_test)"
    ]
   },
   {
@@ -1286,12 +1262,11 @@
    "id": "5538dc17",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.634500Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.634318Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.638177Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.637615Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.526421Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.526330Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.528846Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.528604Z"
+    }
    },
    "outputs": [
     {
@@ -1306,7 +1281,7 @@
     }
    ],
    "source": [
-    "np.mean(labels == L_test), np.mean(labels != L_test)\n"
+    "np.mean(labels == L_test), np.mean(labels != L_test)"
    ]
   },
   {
@@ -1344,12 +1319,11 @@
    "id": "016110f9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.640626Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.640465Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.650498Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.649952Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.530203Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.530112Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.537975Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.537708Z"
+    }
    },
    "outputs": [
     {
@@ -1420,7 +1394,7 @@
     "probs = results.predict(exog=X_test)\n",
     "labels = np.array(['Down']*252)\n",
     "labels[probs>0.5] = 'Up'\n",
-    "confusion_table(labels, L_test)\n"
+    "confusion_table(labels, L_test)"
    ]
   },
   {
@@ -1438,10 +1412,10 @@
    "id": "70984cd6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.653494Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.652768Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.656801Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.656039Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.539380Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.539287Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.541459Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.541223Z"
     }
    },
    "outputs": [
@@ -1457,7 +1431,7 @@
     }
    ],
    "source": [
-    "(35+106)/252,106/(106+76)\n"
+    "(35+106)/252,106/(106+76)"
    ]
   },
   {
@@ -1492,12 +1466,11 @@
    "id": "4b745fd8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.659702Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.659530Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.664523Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.664140Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.542822Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.542726Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.546307Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.545991Z"
+    }
    },
    "outputs": [
     {
@@ -1517,7 +1490,7 @@
     "newdata = pd.DataFrame({'Lag1':[1.2, 1.5],\n",
     "                        'Lag2':[1.1, -0.8]});\n",
     "newX = model.transform(newdata)\n",
-    "results.predict(newX)\n"
+    "results.predict(newX)"
    ]
   },
   {
@@ -1544,15 +1517,15 @@
    "id": "cd533313",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.668435Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.668202Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.670675Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.670317Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.547769Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.547668Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.549398Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.549116Z"
     }
    },
    "outputs": [],
    "source": [
-    "lda = LDA(store_covariance=True)\n"
+    "lda = LDA(store_covariance=True)"
    ]
   },
   {
@@ -1572,18 +1545,17 @@
    "id": "953dc6ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.673288Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.673119Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.681150Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.680147Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.550783Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.550696Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.556779Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.556529Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearDiscriminantAnalysis(store_covariance=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>LinearDiscriminantAnalysis(store_covariance=True)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearDiscriminantAnalysis(store_covariance=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>LinearDiscriminantAnalysis(store_covariance=True)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "LinearDiscriminantAnalysis(store_covariance=True)"
@@ -1597,7 +1569,7 @@
    "source": [
     "X_train, X_test = [M.drop(columns=['intercept'])\n",
     "                   for M in [X_train, X_test]]\n",
-    "lda.fit(X_train, L_train)\n"
+    "lda.fit(X_train, L_train)"
    ]
   },
   {
@@ -1628,12 +1600,11 @@
    "id": "8917085e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.683917Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.683495Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.686711Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.686302Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.558188Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.558090Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.560221Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.559968Z"
+    }
    },
    "outputs": [
     {
@@ -1649,7 +1620,7 @@
     }
    ],
    "source": [
-    "lda.means_\n"
+    "lda.means_"
    ]
   },
   {
@@ -1669,12 +1640,11 @@
    "id": "efb3e9bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.689572Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.689426Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.693007Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.692236Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.561595Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.561505Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.563532Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.563268Z"
+    }
    },
    "outputs": [
     {
@@ -1689,7 +1659,7 @@
     }
    ],
    "source": [
-    "lda.classes_\n"
+    "lda.classes_"
    ]
   },
   {
@@ -1698,7 +1668,7 @@
    "metadata": {},
    "source": [
     "The LDA output indicates that $\\hat\\pi_{Down}=0.492$ and\n",
-    "$\\hat\\pi_{Up}=0.508$.\n"
+    "$\\hat\\pi_{Up}=0.508$."
    ]
   },
   {
@@ -1707,12 +1677,11 @@
    "id": "efce91fb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.698435Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.698251Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.701054Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.700738Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.564867Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.564778Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.566827Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.566591Z"
+    }
    },
    "outputs": [
     {
@@ -1727,7 +1696,7 @@
     }
    ],
    "source": [
-    "lda.priors_\n"
+    "lda.priors_"
    ]
   },
   {
@@ -1744,10 +1713,10 @@
    "id": "e33cd6c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.703175Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.702968Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.706047Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.705603Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.568191Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.568102Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.570124Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.569904Z"
     }
    },
    "outputs": [
@@ -1764,7 +1733,7 @@
     }
    ],
    "source": [
-    "lda.scalings_\n"
+    "lda.scalings_"
    ]
   },
   {
@@ -1782,15 +1751,15 @@
    "id": "dc8a5178",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.708404Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.708240Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.711274Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.710854Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.571405Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.571320Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.573559Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.573280Z"
     }
    },
    "outputs": [],
    "source": [
-    "lda_pred = lda.predict(X_test)\n"
+    "lda_pred = lda.predict(X_test)"
    ]
   },
   {
@@ -1809,12 +1778,11 @@
    "id": "31a6782b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.713574Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.713379Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.718661Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.718207Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.574872Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.574787Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.578731Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.578477Z"
+    }
    },
    "outputs": [
     {
@@ -1875,7 +1843,7 @@
     }
    ],
    "source": [
-    "confusion_table(lda_pred, L_test)\n"
+    "confusion_table(lda_pred, L_test)"
    ]
   },
   {
@@ -1896,12 +1864,11 @@
    "id": "da1bffa3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.720875Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.720726Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.724610Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.724076Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.580076Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.579991Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.582858Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.582636Z"
+    }
    },
    "outputs": [
     {
@@ -1919,7 +1886,7 @@
     "lda_prob = lda.predict_proba(X_test)\n",
     "np.all(\n",
     "       np.where(lda_prob[:,1] >= 0.5, 'Up','Down') == lda_pred\n",
-    "       )\n"
+    "       )"
    ]
   },
   {
@@ -1940,12 +1907,11 @@
    "id": "3d7eae3c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.726893Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.726728Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.730223Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.729787Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.584203Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.584119Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.586526Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.586262Z"
+    }
    },
    "outputs": [
     {
@@ -1962,7 +1928,7 @@
    "source": [
     "np.all(\n",
     "       [lda.classes_[i] for i in np.argmax(lda_prob, 1)] == lda_pred\n",
-    "       )\n"
+    "       )"
    ]
   },
   {
@@ -1986,10 +1952,10 @@
    "id": "18fa175c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.732568Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.732400Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.735369Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.734923Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.587880Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.587782Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.589993Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.589754Z"
     }
    },
    "outputs": [
@@ -2005,7 +1971,7 @@
     }
    ],
    "source": [
-    "np.sum(lda_prob[:,0] > 0.9)\n"
+    "np.sum(lda_prob[:,0] > 0.9)"
    ]
   },
   {
@@ -2029,7 +1995,7 @@
     "then producing predictions is an explicit design choice of `sklearn`. This uniformity\n",
     "makes it possible to cleanly copy the classifier so that it can be fit\n",
     "on different data; e.g. different training sets arising in cross-validation.\n",
-    "This standard pattern also allows for a predictable formation of workflows.\n"
+    "This standard pattern also allows for a predictable formation of workflows."
    ]
   },
   {
@@ -2051,17 +2017,17 @@
    "id": "62499aad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.737641Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.737376Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.742371Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.741972Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.591434Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.591348Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.595079Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.594814Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>QuadraticDiscriminantAnalysis(store_covariance=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">QuadraticDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>QuadraticDiscriminantAnalysis(store_covariance=True)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>QuadraticDiscriminantAnalysis(store_covariance=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">QuadraticDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>QuadraticDiscriminantAnalysis(store_covariance=True)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "QuadraticDiscriminantAnalysis(store_covariance=True)"
@@ -2074,7 +2040,7 @@
    ],
    "source": [
     "qda = QDA(store_covariance=True)\n",
-    "qda.fit(X_train, L_train)\n"
+    "qda.fit(X_train, L_train)"
    ]
   },
   {
@@ -2091,10 +2057,10 @@
    "id": "296fdd89",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.744511Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.744359Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.747331Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.746954Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.596494Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.596400Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.598668Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.598424Z"
     }
    },
    "outputs": [
@@ -2112,7 +2078,7 @@
     }
    ],
    "source": [
-    "qda.means_, qda.priors_\n"
+    "qda.means_, qda.priors_"
    ]
   },
   {
@@ -2130,12 +2096,11 @@
    "id": "7fbbefb0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.749983Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.749838Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.753016Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.752436Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.600048Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.599961Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.602059Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.601831Z"
+    }
    },
    "outputs": [
     {
@@ -2151,7 +2116,7 @@
     }
    ],
    "source": [
-    "qda.covariance_[0]\n"
+    "qda.covariance_[0]"
    ]
   },
   {
@@ -2172,12 +2137,11 @@
    "id": "03094dcd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.754995Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.754855Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.760423Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.760086Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.603367Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.603277Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.607653Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.607403Z"
+    }
    },
    "outputs": [
     {
@@ -2239,7 +2203,7 @@
    ],
    "source": [
     "qda_pred = qda.predict(X_test)\n",
-    "confusion_table(qda_pred, L_test)\n"
+    "confusion_table(qda_pred, L_test)"
    ]
   },
   {
@@ -2257,12 +2221,11 @@
    "id": "25022d1a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.762503Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.762361Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.765264Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.764918Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.609003Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.608911Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.611157Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.610904Z"
+    }
    },
    "outputs": [
     {
@@ -2277,7 +2240,7 @@
     }
    ],
    "source": [
-    "np.mean(qda_pred == L_test)\n"
+    "np.mean(qda_pred == L_test)"
    ]
   },
   {
@@ -2313,18 +2276,17 @@
    "id": "f73eb202",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.767345Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.767204Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.771774Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.771427Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.612539Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.612452Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.616274Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.616028Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "GaussianNB()"
@@ -2337,7 +2299,7 @@
    ],
    "source": [
     "NB = GaussianNB()\n",
-    "NB.fit(X_train, L_train)\n"
+    "NB.fit(X_train, L_train)"
    ]
   },
   {
@@ -2354,12 +2316,11 @@
    "id": "8e860cc3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.773771Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.773609Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.776397Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.776019Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.617600Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.617514Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.619629Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.619380Z"
+    }
    },
    "outputs": [
     {
@@ -2374,7 +2335,7 @@
     }
    ],
    "source": [
-    "NB.classes_\n"
+    "NB.classes_"
    ]
   },
   {
@@ -2391,12 +2352,11 @@
    "id": "c28bf3b5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.778509Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.778344Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.781156Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.780778Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.621089Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.620997Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.623148Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.622907Z"
+    }
    },
    "outputs": [
     {
@@ -2411,7 +2371,7 @@
     }
    ],
    "source": [
-    "NB.class_prior_\n"
+    "NB.class_prior_"
    ]
   },
   {
@@ -2430,10 +2390,10 @@
    "id": "84a9f086",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.783409Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.783220Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.786972Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.786412Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.624473Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.624387Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.626358Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.626069Z"
     }
    },
    "outputs": [
@@ -2450,7 +2410,7 @@
     }
    ],
    "source": [
-    "NB.theta_\n"
+    "NB.theta_"
    ]
   },
   {
@@ -2467,12 +2427,11 @@
    "id": "885f5165",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.789289Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.789145Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.792294Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.791863Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.627848Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.627740Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.630002Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.629749Z"
+    }
    },
    "outputs": [
     {
@@ -2488,7 +2447,7 @@
     }
    ],
    "source": [
-    "NB.var_\n"
+    "NB.var_"
    ]
   },
   {
@@ -2507,12 +2466,11 @@
    "id": "61473094",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.794766Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.794585Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.798951Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.798539Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.631378Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.631279Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.634759Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.634466Z"
+    }
    },
    "outputs": [
     {
@@ -2529,7 +2487,7 @@
     }
    ],
    "source": [
-    "X_train[L_train == 'Down'].mean()\n"
+    "X_train[L_train == 'Down'].mean()"
    ]
   },
   {
@@ -2546,12 +2504,11 @@
    "id": "db203a23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.801373Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.801183Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.805590Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.805195Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.636250Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.636149Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.639444Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.639188Z"
+    }
    },
    "outputs": [
     {
@@ -2568,7 +2525,7 @@
     }
    ],
    "source": [
-    "X_train[L_train == 'Down'].var(ddof=0)\n"
+    "X_train[L_train == 'Down'].var(ddof=0)"
    ]
   },
   {
@@ -2586,10 +2543,10 @@
    "id": "b387227e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.808068Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.807902Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.813920Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.813575Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.640842Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.640749Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.645740Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.645436Z"
     }
    },
    "outputs": [
@@ -2652,7 +2609,7 @@
    ],
    "source": [
     "nb_labels = NB.predict(X_test)\n",
-    "confusion_table(nb_labels, L_test)\n"
+    "confusion_table(nb_labels, L_test)"
    ]
   },
   {
@@ -2671,12 +2628,11 @@
    "id": "f14ebe61",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.816106Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.815946Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.819614Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.819281Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.647249Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.647145Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.650398Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.650121Z"
+    }
    },
    "outputs": [
     {
@@ -2695,7 +2651,7 @@
     }
    ],
    "source": [
-    "NB.predict_proba(X_test)[:5]\n"
+    "NB.predict_proba(X_test)[:5]"
    ]
   },
   {
@@ -2721,10 +2677,10 @@
    "id": "41a12f4a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.821819Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.821654Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.835418Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.834963Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.651868Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.651765Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.663794Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.663506Z"
     }
    },
    "outputs": [
@@ -2787,9 +2743,10 @@
    ],
    "source": [
     "knn1 = KNeighborsClassifier(n_neighbors=1)\n",
+    "X_train, X_test = [np.asarray(X) for X in [X_train, X_test]]\n",
     "knn1.fit(X_train, L_train)\n",
     "knn1_pred = knn1.predict(X_test)\n",
-    "confusion_table(knn1_pred, L_test)\n"
+    "confusion_table(knn1_pred, L_test)"
    ]
   },
   {
@@ -2808,10 +2765,10 @@
    "id": "1dd2f3e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.840858Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.840380Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.844994Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.844563Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.665323Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.665226Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.667806Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.667554Z"
     }
    },
    "outputs": [
@@ -2827,7 +2784,7 @@
     }
    ],
    "source": [
-    "(83+43)/252, np.mean(knn1_pred == L_test)\n"
+    "(83+43)/252, np.mean(knn1_pred == L_test)"
    ]
   },
   {
@@ -2845,12 +2802,11 @@
    "id": "8ec72efc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.848486Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.848323Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.859379Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.859009Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.669248Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.669155Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.677134Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.676869Z"
+    }
    },
    "outputs": [
     {
@@ -2900,12 +2856,11 @@
    "id": "98fef4ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.862002Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.861826Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.880744Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.880217Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.678643Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.678535Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.696626Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.696361Z"
+    }
    },
    "outputs": [
     {
@@ -2924,7 +2879,7 @@
    "source": [
     "Caravan = load_data('Caravan')\n",
     "Purchase = Caravan.Purchase\n",
-    "Purchase.value_counts()\n"
+    "Purchase.value_counts()"
    ]
   },
   {
@@ -2944,12 +2899,11 @@
    "id": "1af72c0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.883491Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.883258Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.886241Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.885825Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.698073Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.697990Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.700407Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.700135Z"
+    }
    },
    "outputs": [
     {
@@ -2964,7 +2918,7 @@
     }
    ],
    "source": [
-    "348 / 5822\n"
+    "348 / 5822"
    ]
   },
   {
@@ -2981,15 +2935,15 @@
    "id": "9ea841e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.888642Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.888457Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.891757Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.891278Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.701889Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.701794Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.704333Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.704051Z"
     }
    },
    "outputs": [],
    "source": [
-    "feature_df = Caravan.drop(columns=['Purchase'])\n"
+    "feature_df = Caravan.drop(columns=['Purchase'])"
    ]
   },
   {
@@ -3030,18 +2984,17 @@
    "id": "6e26b14f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.893952Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.893809Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.896350Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.895913Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.705712Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.705615Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.707253Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.707032Z"
+    }
    },
    "outputs": [],
    "source": [
     "scaler = StandardScaler(with_mean=True,\n",
     "                        with_std=True,\n",
-    "                        copy=True)\n"
+    "                        copy=True)"
    ]
   },
   {
@@ -3069,17 +3022,16 @@
    "id": "bd178f38",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.898581Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.898422Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.907100Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.906252Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.708620Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.708535Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.715278Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.714990Z"
+    }
    },
    "outputs": [],
    "source": [
     "scaler.fit(feature_df)\n",
-    "X_std = scaler.transform(feature_df)\n"
+    "X_std = scaler.transform(feature_df)"
    ]
   },
   {
@@ -3097,10 +3049,10 @@
    "id": "18929b37",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.909697Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.909376Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.917328Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.916861Z"
+     "iopub.execute_input": "2023-07-26T19:27:23.717139Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.717036Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.724072Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.723790Z"
     }
    },
    "outputs": [
@@ -3130,7 +3082,7 @@
     "feature_std = pd.DataFrame(\n",
     "                 X_std,\n",
     "                 columns=feature_df.columns);\n",
-    "feature_std.std()\n"
+    "feature_std.std()"
    ]
   },
   {
@@ -3153,22 +3105,21 @@
    "id": "9d439d0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.919758Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.919493Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.924297Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.923909Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:23.725961Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.725835Z",
+     "iopub.status.idle": "2023-07-26T19:27:23.729839Z",
+     "shell.execute_reply": "2023-07-26T19:27:23.729531Z"
+    }
    },
    "outputs": [],
    "source": [
     "(X_train,\n",
     " X_test,\n",
     " y_train,\n",
-    " y_test) = train_test_split(feature_std,\n",
+    " y_test) = train_test_split(np.asarray(feature_std),\n",
     "                            Purchase,\n",
     "                            test_size=1000,\n",
-    "                            random_state=0)\n"
+    "                            random_state=0)"
    ]
   },
   {
@@ -3187,12 +3138,11 @@
    "id": "9b8c0171",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.926683Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.926533Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.979689Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.979266Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:23.731290Z",
+     "iopub.status.busy": "2023-07-26T19:27:23.731180Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.053534Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.052786Z"
+    }
    },
    "outputs": [
     {
@@ -3209,7 +3159,7 @@
    "source": [
     "knn1 = KNeighborsClassifier(n_neighbors=1)\n",
     "knn1_pred = knn1.fit(X_train, y_train).predict(X_test)\n",
-    "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")\n"
+    "np.mean(y_test != knn1_pred), np.mean(y_test != \"No\")"
    ]
   },
   {
@@ -3240,10 +3190,10 @@
    "id": "f31b6e26",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.982811Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.982601Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.989512Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.989071Z"
+     "iopub.execute_input": "2023-07-26T19:27:24.055740Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.055564Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.062728Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.062388Z"
     }
    },
    "outputs": [
@@ -3305,7 +3255,7 @@
     }
    ],
    "source": [
-    "confusion_table(knn1_pred, y_test)\n"
+    "confusion_table(knn1_pred, y_test)"
    ]
   },
   {
@@ -3325,12 +3275,11 @@
    "id": "4a8c37aa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.992418Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.992103Z",
-     "iopub.status.idle": "2023-07-26T05:16:53.995535Z",
-     "shell.execute_reply": "2023-07-26T05:16:53.995078Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:24.064678Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.064559Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.067018Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.066706Z"
+    }
    },
    "outputs": [
     {
@@ -3369,12 +3318,11 @@
    "id": "153662f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:53.999510Z",
-     "iopub.status.busy": "2023-07-26T05:16:53.999082Z",
-     "iopub.status.idle": "2023-07-26T05:16:54.130823Z",
-     "shell.execute_reply": "2023-07-26T05:16:54.130318Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:24.068844Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.068735Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.146523Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.146183Z"
+    }
    },
    "outputs": [
     {
@@ -3402,7 +3350,7 @@
     "          K,\n",
     "          pred,\n",
     "          did_rent,\n",
-    "          did_rent / pred))\n"
+    "          did_rent / pred))"
    ]
   },
   {
@@ -3436,10 +3384,10 @@
    "id": "55b99a3f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:54.134280Z",
-     "iopub.status.busy": "2023-07-26T05:16:54.133924Z",
-     "iopub.status.idle": "2023-07-26T05:16:54.790553Z",
-     "shell.execute_reply": "2023-07-26T05:16:54.788632Z"
+     "iopub.execute_input": "2023-07-26T19:27:24.148487Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.148362Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.792100Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.786251Z"
     }
    },
    "outputs": [
@@ -3505,7 +3453,7 @@
     "logit.fit(X_train, y_train)\n",
     "logit_pred = logit.predict_proba(X_test)\n",
     "logit_labels = np.where(logit_pred[:,1] > 5, 'Yes', 'No')\n",
-    "confusion_table(logit_labels, y_test)\n"
+    "confusion_table(logit_labels, y_test)"
    ]
   },
   {
@@ -3533,10 +3481,10 @@
    "id": "0c2ea9f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:54.800869Z",
-     "iopub.status.busy": "2023-07-26T05:16:54.800067Z",
-     "iopub.status.idle": "2023-07-26T05:16:54.901396Z",
-     "shell.execute_reply": "2023-07-26T05:16:54.824967Z"
+     "iopub.execute_input": "2023-07-26T19:27:24.799448Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.798879Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.838692Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.825974Z"
     }
    },
    "outputs": [
@@ -3608,12 +3556,11 @@
    "id": "3a6bf0a4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:55.014712Z",
-     "iopub.status.busy": "2023-07-26T05:16:55.014300Z",
-     "iopub.status.idle": "2023-07-26T05:16:55.022432Z",
-     "shell.execute_reply": "2023-07-26T05:16:55.021283Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:24.844349Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.843872Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.869029Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.867200Z"
+    }
    },
    "outputs": [
     {
@@ -3628,7 +3575,7 @@
     }
    ],
    "source": [
-    "9/(20+9)\n"
+    "9/(20+9)"
    ]
   },
   {
@@ -3648,16 +3595,15 @@
    "id": "769300df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:55.026479Z",
-     "iopub.status.busy": "2023-07-26T05:16:55.026122Z",
-     "iopub.status.idle": "2023-07-26T05:16:55.040298Z",
-     "shell.execute_reply": "2023-07-26T05:16:55.039107Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:24.892251Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.891727Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.923108Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.920296Z"
+    }
    },
    "outputs": [],
    "source": [
-    "Bike = load_data('Bikeshare')\n"
+    "Bike = load_data('Bikeshare')"
    ]
   },
   {
@@ -3674,10 +3620,10 @@
    "id": "02b351f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:55.043729Z",
-     "iopub.status.busy": "2023-07-26T05:16:55.043547Z",
-     "iopub.status.idle": "2023-07-26T05:16:55.047018Z",
-     "shell.execute_reply": "2023-07-26T05:16:55.046428Z"
+     "iopub.execute_input": "2023-07-26T19:27:24.928637Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.928214Z",
+     "iopub.status.idle": "2023-07-26T19:27:24.937844Z",
+     "shell.execute_reply": "2023-07-26T19:27:24.935712Z"
     }
    },
    "outputs": [
@@ -3697,7 +3643,7 @@
     }
    ],
    "source": [
-    "Bike.shape, Bike.columns\n"
+    "Bike.shape, Bike.columns"
    ]
   },
   {
@@ -3716,10 +3662,10 @@
    "id": "926c908d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:55.049121Z",
-     "iopub.status.busy": "2023-07-26T05:16:55.049011Z",
-     "iopub.status.idle": "2023-07-26T05:16:55.569728Z",
-     "shell.execute_reply": "2023-07-26T05:16:55.567494Z"
+     "iopub.execute_input": "2023-07-26T19:27:24.942917Z",
+     "iopub.status.busy": "2023-07-26T19:27:24.942591Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.051777Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.050957Z"
     }
    },
    "outputs": [
@@ -4092,7 +4038,7 @@
     "        'weathersit']).fit_transform(Bike)\n",
     "Y = Bike['bikers']\n",
     "M_lm = sm.OLS(Y, X).fit()\n",
-    "summarize(M_lm)\n"
+    "summarize(M_lm)"
    ]
   },
   {
@@ -4120,17 +4066,16 @@
    "id": "064b24d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:55.575305Z",
-     "iopub.status.busy": "2023-07-26T05:16:55.575124Z",
-     "iopub.status.idle": "2023-07-26T05:16:55.582612Z",
-     "shell.execute_reply": "2023-07-26T05:16:55.580612Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:25.054889Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.054711Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.061847Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.058857Z"
+    }
    },
    "outputs": [],
    "source": [
     "hr_encode = contrast('hr', 'sum')\n",
-    "mnth_encode = contrast('mnth', 'sum')\n"
+    "mnth_encode = contrast('mnth', 'sum')"
    ]
   },
   {
@@ -4147,10 +4092,10 @@
    "id": "27c1aa54",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:55.591223Z",
-     "iopub.status.busy": "2023-07-26T05:16:55.589141Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.075120Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.073529Z"
+     "iopub.execute_input": "2023-07-26T19:27:25.066413Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.066041Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.149322Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.148584Z"
     }
    },
    "outputs": [
@@ -4557,10 +4502,10 @@
    "id": "dd0114c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.092324Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.087118Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.137844Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.135218Z"
+     "iopub.execute_input": "2023-07-26T19:27:25.152795Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.152483Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.164824Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.163674Z"
     }
    },
    "outputs": [
@@ -4576,7 +4521,7 @@
     }
    ],
    "source": [
-    "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)\n"
+    "np.sum((M_lm.fittedvalues - M2_lm.fittedvalues)**2)"
    ]
   },
   {
@@ -4594,12 +4539,11 @@
    "id": "292585a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.147457Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.146798Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.159395Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.157990Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:25.170191Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.169889Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.183053Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.182057Z"
+    }
    },
    "outputs": [
     {
@@ -4614,7 +4558,7 @@
     }
    ],
    "source": [
-    "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)\n"
+    "np.allclose(M_lm.fittedvalues, M2_lm.fittedvalues)"
    ]
   },
   {
@@ -4637,12 +4581,11 @@
    "id": "e05e0575",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.169355Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.168352Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.190269Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.186463Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:25.188494Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.186819Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.199572Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.198867Z"
+    }
    },
    "outputs": [
     {
@@ -4669,7 +4612,7 @@
    ],
    "source": [
     "coef_month = S2[S2.index.str.contains('mnth')]['coef']\n",
-    "coef_month\n"
+    "coef_month"
    ]
   },
   {
@@ -4686,12 +4629,11 @@
    "id": "9f04a25e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.213429Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.210143Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.249114Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.246382Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:25.204709Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.203639Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.216352Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.215638Z"
+    }
    },
    "outputs": [
     {
@@ -4725,7 +4667,7 @@
     "                                  index=['mnth[Dec]'\n",
     "                                 ])\n",
     "                       ])\n",
-    "coef_month\n"
+    "coef_month"
    ]
   },
   {
@@ -4743,10 +4685,10 @@
    "id": "20c7c054",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.310254Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.306244Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.486315Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.485755Z"
+     "iopub.execute_input": "2023-07-26T19:27:25.221756Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.220314Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.357663Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.356885Z"
     }
    },
    "outputs": [
@@ -4768,7 +4710,7 @@
     "ax_month.set_xticks(x_month)\n",
     "ax_month.set_xticklabels([l[5] for l in coef_month.index], fontsize=20)\n",
     "ax_month.set_xlabel('Month', fontsize=20)\n",
-    "ax_month.set_ylabel('Coefficient', fontsize=20);\n"
+    "ax_month.set_ylabel('Coefficient', fontsize=20);"
    ]
   },
   {
@@ -4785,10 +4727,10 @@
    "id": "3f83f4bf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.489411Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.489143Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.493292Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.492665Z"
+     "iopub.execute_input": "2023-07-26T19:27:25.361871Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.361292Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.370490Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.369441Z"
     }
    },
    "outputs": [],
@@ -4797,7 +4739,7 @@
     "coef_hr = coef_hr.reindex(['hr[{0}]'.format(h) for h in range(23)])\n",
     "coef_hr = pd.concat([coef_hr,\n",
     "                     pd.Series([-coef_hr.sum()], index=['hr[23]'])\n",
-    "                    ])\n"
+    "                    ])"
    ]
   },
   {
@@ -4814,10 +4756,10 @@
    "id": "86e9a741",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.496145Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.495966Z",
-     "iopub.status.idle": "2023-07-26T05:16:56.609790Z",
-     "shell.execute_reply": "2023-07-26T05:16:56.609280Z"
+     "iopub.execute_input": "2023-07-26T19:27:25.374812Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.373932Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.488198Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.487892Z"
     }
    },
    "outputs": [
@@ -4839,7 +4781,7 @@
     "ax_hr.set_xticks(x_hr[::2])\n",
     "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n",
     "ax_hr.set_xlabel('Hour', fontsize=20)\n",
-    "ax_hr.set_ylabel('Coefficient', fontsize=20);\n"
+    "ax_hr.set_ylabel('Coefficient', fontsize=20);"
    ]
   },
   {
@@ -4860,15 +4802,15 @@
    "id": "a9448278",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:56.612317Z",
-     "iopub.status.busy": "2023-07-26T05:16:56.612179Z",
-     "iopub.status.idle": "2023-07-26T05:16:57.099663Z",
-     "shell.execute_reply": "2023-07-26T05:16:57.097888Z"
+     "iopub.execute_input": "2023-07-26T19:27:25.489915Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.489804Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.572268Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.570870Z"
     }
    },
    "outputs": [],
    "source": [
-    "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()\n"
+    "M_pois = sm.GLM(Y, X2, family=sm.families.Poisson()).fit()"
    ]
   },
   {
@@ -4885,12 +4827,11 @@
    "id": "ec8505be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:57.113476Z",
-     "iopub.status.busy": "2023-07-26T05:16:57.110476Z",
-     "iopub.status.idle": "2023-07-26T05:16:57.156419Z",
-     "shell.execute_reply": "2023-07-26T05:16:57.148075Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:25.576745Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.576416Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.615858Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.605640Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -4919,19 +4860,18 @@
    "id": "0b8a5edf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:57.169051Z",
-     "iopub.status.busy": "2023-07-26T05:16:57.167599Z",
-     "iopub.status.idle": "2023-07-26T05:16:57.482769Z",
-     "shell.execute_reply": "2023-07-26T05:16:57.482137Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:25.620368Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.620014Z",
+     "iopub.status.idle": "2023-07-26T19:27:25.900780Z",
+     "shell.execute_reply": "2023-07-26T19:27:25.900449Z"
+    }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_69056/3779905754.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_5623/3779510511.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n"
      ]
     },
@@ -4956,7 +4896,7 @@
     "ax_hr.plot(x_hr, coef_hr, marker='o', ms=10)\n",
     "ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n",
     "ax_hr.set_xlabel('Hour', fontsize=20)\n",
-    "ax_hr.set_ylabel('Coefficient', fontsize=20);\n"
+    "ax_hr.set_ylabel('Coefficient', fontsize=20);"
    ]
   },
   {
@@ -4976,10 +4916,10 @@
    "id": "d487c7ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:57.485339Z",
-     "iopub.status.busy": "2023-07-26T05:16:57.484975Z",
-     "iopub.status.idle": "2023-07-26T05:16:57.605612Z",
-     "shell.execute_reply": "2023-07-26T05:16:57.605058Z"
+     "iopub.execute_input": "2023-07-26T19:27:25.902592Z",
+     "iopub.status.busy": "2023-07-26T19:27:25.902471Z",
+     "iopub.status.idle": "2023-07-26T19:27:26.007983Z",
+     "shell.execute_reply": "2023-07-26T19:27:26.007659Z"
     }
    },
    "outputs": [
@@ -5002,7 +4942,7 @@
     "ax.set_xlabel('Linear Regression Fit', fontsize=20)\n",
     "ax.set_ylabel('Poisson Regression Fit', fontsize=20)\n",
     "ax.axline([0,0], c='black', linewidth=3,\n",
-    "          linestyle='--', slope=1);\n"
+    "          linestyle='--', slope=1);"
    ]
   },
   {
@@ -5021,16 +4961,20 @@
     "with `family=sm.families.Binomial()` to perform logistic regression. Other\n",
     "choices for the `family` argument can be used to fit other types\n",
     "of GLMs. For instance, `family=sm.families.Gamma()` fits a Gamma regression\n",
-    "model.\n",
-    "\n"
+    "model."
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
+  },
+  "kernelspec": {
+   "display_name": "Python3 (islp_freeze_311)",
+   "language": "python",
+   "name": "islp_freeze_311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -5042,7 +4986,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch5-resample-lab.ipynb b/Ch5-resample-lab.ipynb
index 787ce82..35fc64d 100644
--- a/Ch5-resample-lab.ipynb
+++ b/Ch5-resample-lab.ipynb
@@ -5,9 +5,7 @@
    "id": "3a3f2f85",
    "metadata": {},
    "source": [
-    "\n",
-    "# Chapter 5\n",
-    "\n"
+    "# Chapter 5"
    ]
   },
   {
@@ -29,12 +27,11 @@
    "id": "60fad148",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:58.904948Z",
-     "iopub.status.busy": "2023-07-26T05:16:58.904833Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.817459Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.816977Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:27.380786Z",
+     "iopub.status.busy": "2023-07-26T19:27:27.380697Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.278575Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.278161Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -44,7 +41,7 @@
     "from ISLP.models import (ModelSpec as MS,\n",
     "                         summarize,\n",
     "                         poly)\n",
-    "from sklearn.model_selection import train_test_split\n"
+    "from sklearn.model_selection import train_test_split"
    ]
   },
   {
@@ -61,12 +58,11 @@
    "id": "2478aeb4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.820190Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.819944Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.822616Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.822236Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:28.280720Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.280499Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.282928Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.282654Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -76,7 +72,7 @@
     "      KFold,\n",
     "      ShuffleSplit)\n",
     "from sklearn.base import clone\n",
-    "from ISLP.models import sklearn_sm\n"
+    "from ISLP.models import sklearn_sm"
    ]
   },
   {
@@ -96,7 +92,7 @@
     "when performing operations like this that contain an\n",
     "element of randomness, so that the results obtained can be reproduced\n",
     "precisely at a later time. We set the random seed of the splitter\n",
-    "with the argument `random_state=0`. "
+    "with the argument `random_state=0`."
    ]
   },
   {
@@ -105,10 +101,10 @@
    "id": "99c95faf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.824651Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.824498Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.830661Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.830199Z"
+     "iopub.execute_input": "2023-07-26T19:27:28.284475Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.284367Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.289789Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.289527Z"
     }
    },
    "outputs": [],
@@ -116,7 +112,7 @@
     "Auto = load_data('Auto')\n",
     "Auto_train, Auto_valid = train_test_split(Auto,\n",
     "                                         test_size=196,\n",
-    "                                         random_state=0)\n"
+    "                                         random_state=0)"
    ]
   },
   {
@@ -133,10 +129,10 @@
    "id": "41b0717d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.832904Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.832740Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.837926Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.837164Z"
+     "iopub.execute_input": "2023-07-26T19:27:28.291330Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.291243Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.295395Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.295119Z"
     }
    },
    "outputs": [],
@@ -145,7 +141,7 @@
     "X_train = hp_mm.fit_transform(Auto_train)\n",
     "y_train = Auto_train['mpg']\n",
     "model = sm.OLS(y_train, X_train)\n",
-    "results = model.fit()\n"
+    "results = model.fit()"
    ]
   },
   {
@@ -163,10 +159,10 @@
    "id": "d7ea3c0d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.840693Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.840426Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.847201Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.846618Z"
+     "iopub.execute_input": "2023-07-26T19:27:28.296942Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.296835Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.301179Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.300912Z"
     }
    },
    "outputs": [
@@ -185,7 +181,7 @@
     "X_valid = hp_mm.transform(Auto_valid)\n",
     "y_valid = Auto_valid['mpg']\n",
     "valid_pred = results.predict(X_valid)\n",
-    "np.mean((y_valid - valid_pred)**2)\n"
+    "np.mean((y_valid - valid_pred)**2)"
    ]
   },
   {
@@ -207,10 +203,10 @@
    "id": "a02a2d05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.849828Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.849638Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.852938Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.852364Z"
+     "iopub.execute_input": "2023-07-26T19:27:28.302713Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.302612Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.304865Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.304576Z"
     }
    },
    "outputs": [],
@@ -230,7 +226,7 @@
     "   results = sm.OLS(y_train, X_train).fit()\n",
     "   test_pred = results.predict(X_test)\n",
     "\n",
-    "   return np.mean((y_test - test_pred)**2)\n"
+    "   return np.mean((y_test - test_pred)**2)"
    ]
   },
   {
@@ -250,10 +246,10 @@
    "id": "51d93dea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.855432Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.855267Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.867911Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.867517Z"
+     "iopub.execute_input": "2023-07-26T19:27:28.306297Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.306199Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.316464Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.316226Z"
     }
    },
    "outputs": [
@@ -275,7 +271,7 @@
     "                       'mpg',\n",
     "                       Auto_train,\n",
     "                       Auto_valid)\n",
-    "MSE\n"
+    "MSE"
    ]
   },
   {
@@ -294,10 +290,10 @@
    "id": "83432f06",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.870663Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.870321Z",
-     "iopub.status.idle": "2023-07-26T05:16:59.884091Z",
-     "shell.execute_reply": "2023-07-26T05:16:59.883749Z"
+     "iopub.execute_input": "2023-07-26T19:27:28.317859Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.317761Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.327969Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.327728Z"
     }
    },
    "outputs": [
@@ -377,12 +373,11 @@
    "id": "bcfc433f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:16:59.886618Z",
-     "iopub.status.busy": "2023-07-26T05:16:59.886459Z",
-     "iopub.status.idle": "2023-07-26T05:17:00.662796Z",
-     "shell.execute_reply": "2023-07-26T05:17:00.662228Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:28.329469Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.329375Z",
+     "iopub.status.idle": "2023-07-26T19:27:28.889681Z",
+     "shell.execute_reply": "2023-07-26T19:27:28.889389Z"
+    }
    },
    "outputs": [
     {
@@ -405,7 +400,7 @@
     "                            Y,\n",
     "                            cv=Auto.shape[0])\n",
     "cv_err = np.mean(cv_results['test_score'])\n",
-    "cv_err\n"
+    "cv_err"
    ]
   },
   {
@@ -445,12 +440,11 @@
    "id": "f951ffc8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:00.665287Z",
-     "iopub.status.busy": "2023-07-26T05:17:00.665106Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.422477Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.422022Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:28.891330Z",
+     "iopub.status.busy": "2023-07-26T19:27:28.891213Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.477436Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.477151Z"
+    }
    },
    "outputs": [
     {
@@ -475,7 +469,7 @@
     "                          Y,\n",
     "                          cv=Auto.shape[0])\n",
     "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error\n"
+    "cv_error"
    ]
   },
   {
@@ -493,7 +487,7 @@
     "It has two arrays as\n",
     "arguments, and then forms a larger\n",
     "array where the operation is applied to each pair of elements of the\n",
-    "two arrays. "
+    "two arrays."
    ]
   },
   {
@@ -502,10 +496,10 @@
    "id": "e3610b5a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.424821Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.424682Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.427733Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.427314Z"
+     "iopub.execute_input": "2023-07-26T19:27:29.479102Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.478988Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.481386Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.481131Z"
     }
    },
    "outputs": [
@@ -525,7 +519,7 @@
    "source": [
     "A = np.array([3, 5, 9])\n",
     "B = np.array([2, 4])\n",
-    "np.add.outer(A, B)\n"
+    "np.add.outer(A, B)"
    ]
   },
   {
@@ -544,12 +538,11 @@
    "id": "1627460d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.429812Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.429700Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.457253Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.456715Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:29.482898Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.482801Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.504137Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.503881Z"
+    }
    },
    "outputs": [
     {
@@ -575,7 +568,7 @@
     "                          Y,\n",
     "                          cv=cv)\n",
     "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error\n"
+    "cv_error"
    ]
   },
   {
@@ -609,12 +602,11 @@
    "id": "8a636468",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.460096Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.459902Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.466995Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.466500Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:29.505802Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.505691Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.511368Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.511053Z"
+    }
    },
    "outputs": [
     {
@@ -636,7 +628,7 @@
     "                         Auto.drop(['mpg'], axis=1),\n",
     "                         Auto['mpg'],\n",
     "                         cv=validation);\n",
-    "results['test_score']\n"
+    "results['test_score']"
    ]
   },
   {
@@ -653,10 +645,10 @@
    "id": "746aeccd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.468986Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.468832Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.495719Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.495353Z"
+     "iopub.execute_input": "2023-07-26T19:27:29.512965Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.512857Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.534338Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.534051Z"
     }
    },
    "outputs": [
@@ -679,7 +671,7 @@
     "                         Auto.drop(['mpg'], axis=1),\n",
     "                         Auto['mpg'],\n",
     "                         cv=validation)\n",
-    "results['test_score'].mean(), results['test_score'].std()\n"
+    "results['test_score'].mean(), results['test_score'].std()"
    ]
   },
   {
@@ -727,12 +719,11 @@
    "id": "daa53d0c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.498336Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.498116Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.504124Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.503427Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:29.535910Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.535819Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.539127Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.538860Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -740,7 +731,7 @@
     "def alpha_func(D, idx):\n",
     "   cov_ = np.cov(D[['X','Y']].loc[idx], rowvar=False)\n",
     "   return ((cov_[1,1] - cov_[0,1]) /\n",
-    "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))\n"
+    "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))"
    ]
   },
   {
@@ -761,10 +752,10 @@
    "id": "578c9564",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.507072Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.506919Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.510768Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.510184Z"
+     "iopub.execute_input": "2023-07-26T19:27:29.540591Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.540502Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.543480Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.543192Z"
     }
    },
    "outputs": [
@@ -800,12 +791,11 @@
    "id": "5754d6d5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.512875Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.512759Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.516640Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.516258Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:29.544876Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.544785Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.548136Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.547873Z"
+    }
    },
    "outputs": [
     {
@@ -843,12 +833,11 @@
    "id": "8320a49c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.519014Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.518877Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.521841Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.521433Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:29.549543Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.549456Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.551815Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.551542Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -879,7 +868,7 @@
     "unimportant and simply makes sure  the loop is executed `B` times.\n",
     "\n",
     "Let’s use our function to evaluate the accuracy of our\n",
-    "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications. "
+    "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications."
    ]
   },
   {
@@ -888,10 +877,10 @@
    "id": "e656aa1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.523933Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.523801Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.855578Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.855121Z"
+     "iopub.execute_input": "2023-07-26T19:27:29.553209Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.553118Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.837271Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.836983Z"
     }
    },
    "outputs": [
@@ -911,7 +900,7 @@
     "                   Portfolio,\n",
     "                   B=1000,\n",
     "                   seed=0)\n",
-    "alpha_SE\n"
+    "alpha_SE"
    ]
   },
   {
@@ -954,12 +943,11 @@
    "id": "c5d14195",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.857907Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.857723Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.860405Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.859990Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:29.838930Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.838793Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.840975Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.840683Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -989,16 +977,15 @@
    "id": "7e0523f0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.862645Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.862482Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.864654Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.864299Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:29.842453Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.842355Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.844134Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.843887Z"
+    }
    },
    "outputs": [],
    "source": [
-    "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')\n"
+    "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')"
    ]
   },
   {
@@ -1022,12 +1009,11 @@
    "id": "32836e93",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.866661Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.866518Z",
-     "iopub.status.idle": "2023-07-26T05:17:01.885141Z",
-     "shell.execute_reply": "2023-07-26T05:17:01.884669Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:29.845545Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.845443Z",
+     "iopub.status.idle": "2023-07-26T19:27:29.860387Z",
+     "shell.execute_reply": "2023-07-26T19:27:29.860140Z"
+    }
    },
    "outputs": [
     {
@@ -1055,7 +1041,7 @@
     "np.array([hp_func(Auto,\n",
     "          rng.choice(392,\n",
     "                     392,\n",
-    "                     replace=True)) for _ in range(10)])\n"
+    "                     replace=True)) for _ in range(10)])"
    ]
   },
   {
@@ -1073,12 +1059,11 @@
    "id": "14ce3afa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:01.887739Z",
-     "iopub.status.busy": "2023-07-26T05:17:01.887564Z",
-     "iopub.status.idle": "2023-07-26T05:17:03.352633Z",
-     "shell.execute_reply": "2023-07-26T05:17:03.352273Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:29.861819Z",
+     "iopub.status.busy": "2023-07-26T19:27:29.861721Z",
+     "iopub.status.idle": "2023-07-26T19:27:31.013897Z",
+     "shell.execute_reply": "2023-07-26T19:27:31.013532Z"
+    }
    },
    "outputs": [
     {
@@ -1099,7 +1084,7 @@
     "                Auto,\n",
     "                B=1000,\n",
     "                seed=10)\n",
-    "hp_se\n"
+    "hp_se"
    ]
   },
   {
@@ -1123,12 +1108,11 @@
    "id": "6b1213ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:03.356355Z",
-     "iopub.status.busy": "2023-07-26T05:17:03.356184Z",
-     "iopub.status.idle": "2023-07-26T05:17:03.453023Z",
-     "shell.execute_reply": "2023-07-26T05:17:03.452622Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:27:31.015545Z",
+     "iopub.status.busy": "2023-07-26T19:27:31.015428Z",
+     "iopub.status.idle": "2023-07-26T19:27:31.077742Z",
+     "shell.execute_reply": "2023-07-26T19:27:31.077471Z"
+    }
    },
    "outputs": [
     {
@@ -1147,7 +1131,7 @@
    "source": [
     "hp_model.fit(Auto, Auto['mpg'])\n",
     "model_se = summarize(hp_model.results_)['std err']\n",
-    "model_se\n"
+    "model_se"
    ]
   },
   {
@@ -1195,10 +1179,10 @@
    "id": "af99b778",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:03.455469Z",
-     "iopub.status.busy": "2023-07-26T05:17:03.455201Z",
-     "iopub.status.idle": "2023-07-26T05:17:05.757186Z",
-     "shell.execute_reply": "2023-07-26T05:17:05.756703Z"
+     "iopub.execute_input": "2023-07-26T19:27:31.079381Z",
+     "iopub.status.busy": "2023-07-26T19:27:31.079238Z",
+     "iopub.status.idle": "2023-07-26T19:27:32.849869Z",
+     "shell.execute_reply": "2023-07-26T19:27:32.849577Z"
     }
    },
    "outputs": [
@@ -1221,7 +1205,7 @@
     "quad_func = partial(boot_OLS,\n",
     "                    quad_model,\n",
     "                    'mpg')\n",
-    "boot_SE(quad_func, Auto, B=1000)\n"
+    "boot_SE(quad_func, Auto, B=1000)"
    ]
   },
   {
@@ -1238,12 +1222,11 @@
    "id": "0206281e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:05.759524Z",
-     "iopub.status.busy": "2023-07-26T05:17:05.759220Z",
-     "iopub.status.idle": "2023-07-26T05:17:05.769493Z",
-     "shell.execute_reply": "2023-07-26T05:17:05.768995Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:27:32.851478Z",
+     "iopub.status.busy": "2023-07-26T19:27:32.851368Z",
+     "iopub.status.idle": "2023-07-26T19:27:32.860591Z",
+     "shell.execute_reply": "2023-07-26T19:27:32.860334Z"
+    }
    },
    "outputs": [
     {
@@ -1263,24 +1246,15 @@
    "source": [
     "M = sm.OLS(Auto['mpg'],\n",
     "           quad_model.fit_transform(Auto))\n",
-    "summarize(M.fit())['std err']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c11a71f",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n"
+    "summarize(M.fit())['std err']"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1292,7 +1266,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch6-varselect-lab.ipynb b/Ch6-varselect-lab.ipynb
index c78185a..7dd9e4a 100644
--- a/Ch6-varselect-lab.ipynb
+++ b/Ch6-varselect-lab.ipynb
@@ -5,7 +5,6 @@
    "id": "1e9ee9d8",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 6"
    ]
   },
@@ -17,7 +16,7 @@
     "# Lab: Linear Models and Regularization Methods\n",
     "In this lab we implement many of the techniques discussed in this chapter.\n",
     "We import some of our libraries at this top\n",
-    "level. "
+    "level."
    ]
   },
   {
@@ -26,10 +25,10 @@
    "id": "564883b2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:07.048830Z",
-     "iopub.status.busy": "2023-07-26T05:17:07.048718Z",
-     "iopub.status.idle": "2023-07-26T05:17:08.239907Z",
-     "shell.execute_reply": "2023-07-26T05:17:08.239413Z"
+     "iopub.execute_input": "2023-07-26T19:29:32.688135Z",
+     "iopub.status.busy": "2023-07-26T19:29:32.688041Z",
+     "iopub.status.idle": "2023-07-26T19:29:35.292586Z",
+     "shell.execute_reply": "2023-07-26T19:29:35.292113Z"
     }
    },
    "outputs": [],
@@ -43,7 +42,7 @@
     "from sklearn.preprocessing import StandardScaler\n",
     "from ISLP import load_data\n",
     "from ISLP.models import ModelSpec as MS\n",
-    "from functools import partial\n"
+    "from functools import partial"
    ]
   },
   {
@@ -61,25 +60,22 @@
    "id": "9aa8d3ee",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:08.242431Z",
-     "iopub.status.busy": "2023-07-26T05:17:08.242231Z",
-     "iopub.status.idle": "2023-07-26T05:17:09.939443Z",
-     "shell.execute_reply": "2023-07-26T05:17:09.938977Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:35.294849Z",
+     "iopub.status.busy": "2023-07-26T19:29:35.294558Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.002209Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.001861Z"
+    }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: l0bnb in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (1.0.0)\r\n",
-      "Requirement already satisfied: numpy>=1.18.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (1.21.6)\r\n",
-      "Requirement already satisfied: scipy>=1.4.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages (from l0bnb) (1.10.1)\r\n",
-      "Requirement already satisfied: numba>=0.53.1 in /Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/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_39/lib/python3.9/site-packages (from numba>=0.53.1->l0bnb) (0.40.1)\r\n",
-      "\u001b[33mDEPRECATION: pytorch-lightning 1.7.7 has a non-standard dependency specifier torch>=1.9.*. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\r\n",
-      "\u001b[0m"
+      "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"
      ]
     }
    ],
@@ -92,7 +88,7 @@
     "      sklearn_selected,\n",
     "      sklearn_selection_path)\n",
     "!pip install l0bnb\n",
-    "from l0bnb import fit_path\n"
+    "from l0bnb import fit_path"
    ]
   },
   {
@@ -127,10 +123,10 @@
    "id": "05371e33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:09.942405Z",
-     "iopub.status.busy": "2023-07-26T05:17:09.942257Z",
-     "iopub.status.idle": "2023-07-26T05:17:09.952925Z",
-     "shell.execute_reply": "2023-07-26T05:17:09.952485Z"
+     "iopub.execute_input": "2023-07-26T19:29:38.004121Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.004003Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.011598Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.011327Z"
     }
    },
    "outputs": [
@@ -147,7 +143,7 @@
    ],
    "source": [
     "Hitters = load_data('Hitters')\n",
-    "np.isnan(Hitters['Salary']).sum()\n"
+    "np.isnan(Hitters['Salary']).sum()"
    ]
   },
   {
@@ -166,12 +162,11 @@
    "id": "aa5bc5b8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:09.955373Z",
-     "iopub.status.busy": "2023-07-26T05:17:09.955215Z",
-     "iopub.status.idle": "2023-07-26T05:17:09.959055Z",
-     "shell.execute_reply": "2023-07-26T05:17:09.958533Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:29:38.013192Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.013094Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.016679Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.016437Z"
+    }
    },
    "outputs": [
     {
@@ -187,7 +182,7 @@
    ],
    "source": [
     "Hitters = Hitters.dropna();\n",
-    "Hitters.shape\n"
+    "Hitters.shape"
    ]
   },
   {
@@ -207,12 +202,11 @@
    "id": "10494837",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:09.961940Z",
-     "iopub.status.busy": "2023-07-26T05:17:09.961736Z",
-     "iopub.status.idle": "2023-07-26T05:17:09.964700Z",
-     "shell.execute_reply": "2023-07-26T05:17:09.964271Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:38.018202Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.018107Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.020134Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.019872Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -221,7 +215,7 @@
     "    n, p = X.shape\n",
     "    Yhat = estimator.predict(X)\n",
     "    RSS = np.sum((Y - Yhat)**2)\n",
-    "    return -(RSS + 2 * p * sigma2) / n \n"
+    "    return -(RSS + 2 * p * sigma2) / n "
    ]
   },
   {
@@ -239,10 +233,10 @@
    "id": "b8173cef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:09.967518Z",
-     "iopub.status.busy": "2023-07-26T05:17:09.967341Z",
-     "iopub.status.idle": "2023-07-26T05:17:09.983184Z",
-     "shell.execute_reply": "2023-07-26T05:17:09.982693Z"
+     "iopub.execute_input": "2023-07-26T19:29:38.021819Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.021680Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.035269Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.034967Z"
     }
    },
    "outputs": [],
@@ -250,7 +244,7 @@
     "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n",
     "Y = np.array(Hitters['Salary'])\n",
     "X = design.transform(Hitters)\n",
-    "sigma2 = OLS(Y,X).fit().scale\n"
+    "sigma2 = OLS(Y,X).fit().scale"
    ]
   },
   {
@@ -267,16 +261,15 @@
    "id": "0cc8d7bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:09.985554Z",
-     "iopub.status.busy": "2023-07-26T05:17:09.985389Z",
-     "iopub.status.idle": "2023-07-26T05:17:09.987653Z",
-     "shell.execute_reply": "2023-07-26T05:17:09.987280Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:38.037153Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.037060Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.038929Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.038618Z"
+    }
    },
    "outputs": [],
    "source": [
-    "neg_Cp = partial(nCp, sigma2)\n"
+    "neg_Cp = partial(nCp, sigma2)"
    ]
   },
   {
@@ -284,7 +277,7 @@
    "id": "3ff5a859",
    "metadata": {},
    "source": [
-    "We can now use `neg_Cp()` as a scorer for model selection.\n"
+    "We can now use `neg_Cp()` as a scorer for model selection."
    ]
   },
   {
@@ -305,17 +298,17 @@
    "id": "440b4c50",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:09.989896Z",
-     "iopub.status.busy": "2023-07-26T05:17:09.989765Z",
-     "iopub.status.idle": "2023-07-26T05:17:09.992061Z",
-     "shell.execute_reply": "2023-07-26T05:17:09.991502Z"
+     "iopub.execute_input": "2023-07-26T19:29:38.041257Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.041110Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.043209Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.042923Z"
     }
    },
    "outputs": [],
    "source": [
     "strategy = Stepwise.first_peak(design,\n",
     "                               direction='forward',\n",
-    "                               max_terms=len(design.terms))\n"
+    "                               max_terms=len(design.terms))"
    ]
   },
   {
@@ -336,10 +329,10 @@
    "id": "b33a8617",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:09.994597Z",
-     "iopub.status.busy": "2023-07-26T05:17:09.994397Z",
-     "iopub.status.idle": "2023-07-26T05:17:10.882860Z",
-     "shell.execute_reply": "2023-07-26T05:17:10.882342Z"
+     "iopub.execute_input": "2023-07-26T19:29:38.044804Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.044694Z",
+     "iopub.status.idle": "2023-07-26T19:29:38.735481Z",
+     "shell.execute_reply": "2023-07-26T19:29:38.735166Z"
     }
    },
    "outputs": [
@@ -376,7 +369,7 @@
     "hitters_MSE = sklearn_selected(OLS,\n",
     "                               strategy)\n",
     "hitters_MSE.fit(Hitters, Y)\n",
-    "hitters_MSE.selected_state_\n"
+    "hitters_MSE.selected_state_"
    ]
   },
   {
@@ -393,10 +386,10 @@
    "id": "e7f554cf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:10.885149Z",
-     "iopub.status.busy": "2023-07-26T05:17:10.884996Z",
-     "iopub.status.idle": "2023-07-26T05:17:11.423862Z",
-     "shell.execute_reply": "2023-07-26T05:17:11.423408Z"
+     "iopub.execute_input": "2023-07-26T19:29:38.737187Z",
+     "iopub.status.busy": "2023-07-26T19:29:38.737059Z",
+     "iopub.status.idle": "2023-07-26T19:29:39.144929Z",
+     "shell.execute_reply": "2023-07-26T19:29:39.144640Z"
     }
    },
    "outputs": [
@@ -425,7 +418,7 @@
     "                               strategy,\n",
     "                               scoring=neg_Cp)\n",
     "hitters_Cp.fit(Hitters, Y)\n",
-    "hitters_Cp.selected_state_\n"
+    "hitters_Cp.selected_state_"
    ]
   },
   {
@@ -459,10 +452,10 @@
    "id": "fb7091b8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:11.426460Z",
-     "iopub.status.busy": "2023-07-26T05:17:11.426313Z",
-     "iopub.status.idle": "2023-07-26T05:17:11.428837Z",
-     "shell.execute_reply": "2023-07-26T05:17:11.428385Z"
+     "iopub.execute_input": "2023-07-26T19:29:39.146740Z",
+     "iopub.status.busy": "2023-07-26T19:29:39.146614Z",
+     "iopub.status.idle": "2023-07-26T19:29:39.148552Z",
+     "shell.execute_reply": "2023-07-26T19:29:39.148289Z"
     }
    },
    "outputs": [],
@@ -470,7 +463,7 @@
     "strategy = Stepwise.fixed_steps(design,\n",
     "                                len(design.terms),\n",
     "                                direction='forward')\n",
-    "full_path = sklearn_selection_path(OLS, strategy)\n"
+    "full_path = sklearn_selection_path(OLS, strategy)"
    ]
   },
   {
@@ -487,12 +480,11 @@
    "id": "56adc728",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:11.431149Z",
-     "iopub.status.busy": "2023-07-26T05:17:11.431007Z",
-     "iopub.status.idle": "2023-07-26T05:17:11.925588Z",
-     "shell.execute_reply": "2023-07-26T05:17:11.925166Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:29:39.150092Z",
+     "iopub.status.busy": "2023-07-26T19:29:39.149998Z",
+     "iopub.status.idle": "2023-07-26T19:29:39.549046Z",
+     "shell.execute_reply": "2023-07-26T19:29:39.548679Z"
+    }
    },
    "outputs": [
     {
@@ -509,7 +501,7 @@
    "source": [
     "full_path.fit(Hitters, Y)\n",
     "Yhat_in = full_path.predict(Hitters)\n",
-    "Yhat_in.shape\n"
+    "Yhat_in.shape"
    ]
   },
   {
@@ -532,12 +524,11 @@
    "id": "387267d9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:11.928605Z",
-     "iopub.status.busy": "2023-07-26T05:17:11.928329Z",
-     "iopub.status.idle": "2023-07-26T05:17:12.168674Z",
-     "shell.execute_reply": "2023-07-26T05:17:12.165742Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:39.550767Z",
+     "iopub.status.busy": "2023-07-26T19:29:39.550657Z",
+     "iopub.status.idle": "2023-07-26T19:29:39.747667Z",
+     "shell.execute_reply": "2023-07-26T19:29:39.746890Z"
+    }
    },
    "outputs": [
     {
@@ -565,7 +556,7 @@
     "              fontsize=20)\n",
     "ax.set_xticks(np.arange(n_steps)[::2])\n",
     "ax.legend()\n",
-    "ax.set_ylim([50000,250000]);\n"
+    "ax.set_ylim([50000,250000]);"
    ]
   },
   {
@@ -601,10 +592,10 @@
    "id": "0047ba88",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:12.180601Z",
-     "iopub.status.busy": "2023-07-26T05:17:12.179903Z",
-     "iopub.status.idle": "2023-07-26T05:17:14.657028Z",
-     "shell.execute_reply": "2023-07-26T05:17:14.656647Z"
+     "iopub.execute_input": "2023-07-26T19:29:39.753242Z",
+     "iopub.status.busy": "2023-07-26T19:29:39.752751Z",
+     "iopub.status.idle": "2023-07-26T19:29:41.762607Z",
+     "shell.execute_reply": "2023-07-26T19:29:41.762318Z"
     }
    },
    "outputs": [
@@ -628,7 +619,7 @@
     "                                Hitters,\n",
     "                                Y,\n",
     "                                cv=kfold)\n",
-    "Yhat_cv.shape\n"
+    "Yhat_cv.shape"
    ]
   },
   {
@@ -653,10 +644,10 @@
    "id": "27b0eb63",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:14.659368Z",
-     "iopub.status.busy": "2023-07-26T05:17:14.659240Z",
-     "iopub.status.idle": "2023-07-26T05:17:14.663478Z",
-     "shell.execute_reply": "2023-07-26T05:17:14.663025Z"
+     "iopub.execute_input": "2023-07-26T19:29:41.764308Z",
+     "iopub.status.busy": "2023-07-26T19:29:41.764195Z",
+     "iopub.status.idle": "2023-07-26T19:29:41.767424Z",
+     "shell.execute_reply": "2023-07-26T19:29:41.767146Z"
     }
    },
    "outputs": [
@@ -677,7 +668,7 @@
     "    errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n",
     "    cv_mse.append(errors.mean(0)) # column means\n",
     "cv_mse = np.array(cv_mse).T\n",
-    "cv_mse.shape\n"
+    "cv_mse.shape"
    ]
   },
   {
@@ -686,7 +677,7 @@
    "metadata": {},
    "source": [
     "We now add the cross-validation error estimates to our MSE plot.\n",
-    "We include the mean error across the five folds, and the estimate of the standard error of the mean. "
+    "We include the mean error across the five folds, and the estimate of the standard error of the mean."
    ]
   },
   {
@@ -695,10 +686,10 @@
    "id": "a45fe587",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:14.665854Z",
-     "iopub.status.busy": "2023-07-26T05:17:14.665694Z",
-     "iopub.status.idle": "2023-07-26T05:17:14.764103Z",
-     "shell.execute_reply": "2023-07-26T05:17:14.763688Z"
+     "iopub.execute_input": "2023-07-26T19:29:41.768951Z",
+     "iopub.status.busy": "2023-07-26T19:29:41.768862Z",
+     "iopub.status.idle": "2023-07-26T19:29:41.856384Z",
+     "shell.execute_reply": "2023-07-26T19:29:41.856056Z"
     }
    },
    "outputs": [
@@ -722,7 +713,7 @@
     "            c='r') # color red\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.legend()\n",
-    "mse_fig\n"
+    "mse_fig"
    ]
   },
   {
@@ -741,12 +732,11 @@
    "id": "c71c2079",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:14.766668Z",
-     "iopub.status.busy": "2023-07-26T05:17:14.766176Z",
-     "iopub.status.idle": "2023-07-26T05:17:15.254311Z",
-     "shell.execute_reply": "2023-07-26T05:17:15.253748Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:41.858093Z",
+     "iopub.status.busy": "2023-07-26T19:29:41.857972Z",
+     "iopub.status.idle": "2023-07-26T19:29:42.250753Z",
+     "shell.execute_reply": "2023-07-26T19:29:42.250451Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -758,7 +748,7 @@
     "                  Y[train_idx])\n",
     "    Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n",
     "    errors = (Yhat_val - Y[test_idx,None])**2\n",
-    "    validation_mse = errors.mean(0)\n"
+    "    validation_mse = errors.mean(0)"
    ]
   },
   {
@@ -775,12 +765,11 @@
    "id": "2ff34edf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:15.257176Z",
-     "iopub.status.busy": "2023-07-26T05:17:15.256855Z",
-     "iopub.status.idle": "2023-07-26T05:17:15.361521Z",
-     "shell.execute_reply": "2023-07-26T05:17:15.360872Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:29:42.252468Z",
+     "iopub.status.busy": "2023-07-26T19:29:42.252383Z",
+     "iopub.status.idle": "2023-07-26T19:29:42.344240Z",
+     "shell.execute_reply": "2023-07-26T19:29:42.343919Z"
+    }
    },
    "outputs": [
     {
@@ -803,7 +792,7 @@
     "ax.set_xticks(np.arange(n_steps)[::2])\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.legend()\n",
-    "mse_fig\n"
+    "mse_fig"
    ]
   },
   {
@@ -819,7 +808,7 @@
     "best subset selection.\n",
     "Instead of constraining the subset to be a given size,\n",
     "this package produces a path of solutions using the subset size as a\n",
-    "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate. \n"
+    "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate."
    ]
   },
   {
@@ -828,18 +817,17 @@
    "id": "845f3fa0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:15.364952Z",
-     "iopub.status.busy": "2023-07-26T05:17:15.364689Z",
-     "iopub.status.idle": "2023-07-26T05:17:15.379444Z",
-     "shell.execute_reply": "2023-07-26T05:17:15.379028Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:42.345827Z",
+     "iopub.status.busy": "2023-07-26T19:29:42.345734Z",
+     "iopub.status.idle": "2023-07-26T19:29:42.357371Z",
+     "shell.execute_reply": "2023-07-26T19:29:42.357099Z"
+    }
    },
    "outputs": [],
    "source": [
     "D = design.fit_transform(Hitters)\n",
     "D = D.drop('intercept', axis=1)\n",
-    "X = np.asarray(D)\n"
+    "X = np.asarray(D)"
    ]
   },
   {
@@ -857,10 +845,10 @@
    "id": "e9f943e7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:15.381427Z",
-     "iopub.status.busy": "2023-07-26T05:17:15.381284Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.000267Z",
-     "shell.execute_reply": "2023-07-26T05:17:17.999357Z"
+     "iopub.execute_input": "2023-07-26T19:29:42.358881Z",
+     "iopub.status.busy": "2023-07-26T19:29:42.358799Z",
+     "iopub.status.idle": "2023-07-26T19:29:47.993575Z",
+     "shell.execute_reply": "2023-07-26T19:29:47.993217Z"
     }
    },
    "outputs": [
@@ -895,7 +883,7 @@
    "source": [
     "path = fit_path(X, \n",
     "                Y,\n",
-    "                max_nonzeros=X.shape[1])\n"
+    "                max_nonzeros=X.shape[1])"
    ]
   },
   {
@@ -912,12 +900,11 @@
    "id": "657ac171",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.003862Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.003705Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.006563Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.006222Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:47.995249Z",
+     "iopub.status.busy": "2023-07-26T19:29:47.995133Z",
+     "iopub.status.idle": "2023-07-26T19:29:47.997730Z",
+     "shell.execute_reply": "2023-07-26T19:29:47.997457Z"
+    }
    },
    "outputs": [
     {
@@ -939,7 +926,7 @@
     }
    ],
    "source": [
-    "path[3]\n"
+    "path[3]"
    ]
   },
   {
@@ -977,417 +964,416 @@
    "id": "e576ad54",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.008980Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.008783Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.078116Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.077656Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:47.999187Z",
+     "iopub.status.busy": "2023-07-26T19:29:47.999082Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.070996Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.070747Z"
+    }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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"
      ]
     },
@@ -1411,7 +1397,7 @@
     "                                 Y,\n",
     "                                 l1_ratio=0.,\n",
     "                                 alphas=lambdas)[1]\n",
-    "soln_array.shape\n"
+    "soln_array.shape"
    ]
   },
   {
@@ -1444,10 +1430,10 @@
    "id": "3ef15192",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.080752Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.080552Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.094265Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.093783Z"
+     "iopub.execute_input": "2023-07-26T19:29:48.072860Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.072754Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.082472Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.082220Z"
     }
    },
    "outputs": [
@@ -1847,7 +1833,7 @@
     "                         columns=D.columns,\n",
     "                         index=-np.log(lambdas))\n",
     "soln_path.index.name = 'negative log(lambda)'\n",
-    "soln_path\n"
+    "soln_path"
    ]
   },
   {
@@ -1866,12 +1852,11 @@
    "id": "40b67c85",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.097009Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.096807Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.336481Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.335993Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:48.083948Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.083852Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.295490Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.295144Z"
+    }
    },
    "outputs": [
     {
@@ -1890,7 +1875,7 @@
     "soln_path.plot(ax=ax, legend=False)\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
     "ax.set_ylabel('Standardized coefficients', fontsize=20)\n",
-    "ax.legend(loc='upper left');\n"
+    "ax.legend(loc='upper left');"
    ]
   },
   {
@@ -1911,10 +1896,10 @@
    "id": "d14bd86c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.338709Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.338591Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.342588Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.342104Z"
+     "iopub.execute_input": "2023-07-26T19:29:48.297203Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.297077Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.302189Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.301932Z"
     }
    },
    "outputs": [
@@ -1951,7 +1936,7 @@
    ],
    "source": [
     "beta_hat = soln_path.loc[soln_path.index[39]]\n",
-    "lambdas[39], beta_hat\n"
+    "lambdas[39], beta_hat"
    ]
   },
   {
@@ -1968,10 +1953,10 @@
    "id": "759e215a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.344904Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.344761Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.347862Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.347417Z"
+     "iopub.execute_input": "2023-07-26T19:29:48.303804Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.303691Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.305916Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.305624Z"
     }
    },
    "outputs": [
@@ -1987,7 +1972,7 @@
     }
    ],
    "source": [
-    "np.linalg.norm(beta_hat)\n"
+    "np.linalg.norm(beta_hat)"
    ]
   },
   {
@@ -2006,12 +1991,11 @@
    "id": "b58d5ccb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.350171Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.350022Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.353142Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.352734Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:48.307347Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.307249Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.309462Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.309210Z"
+    }
    },
    "outputs": [
     {
@@ -2027,7 +2011,7 @@
    ],
    "source": [
     "beta_hat = soln_path.loc[soln_path.index[59]]\n",
-    "lambdas[59], np.linalg.norm(beta_hat)\n"
+    "lambdas[59], np.linalg.norm(beta_hat)"
    ]
   },
   {
@@ -2047,10 +2031,10 @@
    "id": "74b5cae2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.355548Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.355367Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.370341Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.369949Z"
+     "iopub.execute_input": "2023-07-26T19:29:48.310905Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.310803Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.323252Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.322944Z"
     }
    },
    "outputs": [
@@ -2058,14 +2042,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet(alpha=0.24374766133488554, l1_ratio=0)</pre></div></div></div></div></div></div></div>"
       ],
@@ -2083,7 +2067,7 @@
     "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n",
     "scaler = StandardScaler(with_mean=True,  with_std=True)\n",
     "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n",
-    "pipe.fit(X, Y)\n"
+    "pipe.fit(X, Y)"
    ]
   },
   {
@@ -2100,12 +2084,11 @@
    "id": "a89989c3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.372736Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.372583Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.375751Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.375223Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:48.324781Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.324689Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.327207Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.326929Z"
+    }
    },
    "outputs": [
     {
@@ -2120,7 +2103,7 @@
     }
    ],
    "source": [
-    "np.linalg.norm(ridge.coef_)\n"
+    "np.linalg.norm(ridge.coef_)"
    ]
   },
   {
@@ -2147,10 +2130,10 @@
    "id": "3eb41945",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.377750Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.377627Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.385518Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.385025Z"
+     "iopub.execute_input": "2023-07-26T19:29:48.328714Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.328621Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.336141Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.335845Z"
     }
    },
    "outputs": [
@@ -2158,7 +2141,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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"
      ]
     },
@@ -2183,7 +2166,7 @@
     "                             Y,\n",
     "                             scoring='neg_mean_squared_error',\n",
     "                             cv=validation)\n",
-    "-results['test_score']\n"
+    "-results['test_score']"
    ]
   },
   {
@@ -2205,19 +2188,18 @@
    "id": "635bc363",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.387843Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.387682Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.396692Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.396288Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:48.337720Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.337601Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.345014Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.344722Z"
+    }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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"
      ]
     },
@@ -2239,7 +2221,7 @@
     "                             Y,\n",
     "                             scoring='neg_mean_squared_error',\n",
     "                             cv=validation)\n",
-    "-results['test_score']\n"
+    "-results['test_score']"
    ]
   },
   {
@@ -2262,10 +2244,10 @@
    "id": "e117267f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.399267Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.399145Z",
-     "iopub.status.idle": "2023-07-26T05:17:18.894315Z",
-     "shell.execute_reply": "2023-07-26T05:17:18.893964Z"
+     "iopub.execute_input": "2023-07-26T19:29:48.346567Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.346463Z",
+     "iopub.status.idle": "2023-07-26T19:29:48.811069Z",
+     "shell.execute_reply": "2023-07-26T19:29:48.810801Z"
     }
    },
    "outputs": [
@@ -2273,214 +2255,214 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet(alpha=0.005899006046740856, l1_ratio=0)</pre></div></div></div></div></div></div></div>"
       ],
@@ -2502,7 +2484,7 @@
     "                        scoring='neg_mean_squared_error')\n",
     "grid.fit(X, Y)\n",
     "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_\n"
+    "grid.best_estimator_"
    ]
   },
   {
@@ -2519,1026 +2501,1025 @@
    "id": "0037e9e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:18.896714Z",
-     "iopub.status.busy": "2023-07-26T05:17:18.896538Z",
-     "iopub.status.idle": "2023-07-26T05:17:22.201859Z",
-     "shell.execute_reply": "2023-07-26T05:17:22.201489Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:48.812716Z",
+     "iopub.status.busy": "2023-07-26T19:29:48.812609Z",
+     "iopub.status.idle": "2023-07-26T19:29:51.937843Z",
+     "shell.execute_reply": "2023-07-26T19:29:51.937553Z"
+    }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div></div></div></div></div></div></div>"
       ],
@@ -3559,7 +3540,7 @@
     "                        scoring='neg_mean_squared_error')\n",
     "grid.fit(X, Y)\n",
     "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_\n"
+    "grid.best_estimator_"
    ]
   },
   {
@@ -3577,10 +3558,10 @@
    "id": "71f0f5c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:22.203851Z",
-     "iopub.status.busy": "2023-07-26T05:17:22.203701Z",
-     "iopub.status.idle": "2023-07-26T05:17:22.319890Z",
-     "shell.execute_reply": "2023-07-26T05:17:22.318803Z"
+     "iopub.execute_input": "2023-07-26T19:29:51.939433Z",
+     "iopub.status.busy": "2023-07-26T19:29:51.939325Z",
+     "iopub.status.idle": "2023-07-26T19:29:52.041292Z",
+     "shell.execute_reply": "2023-07-26T19:29:52.040967Z"
     }
    },
    "outputs": [
@@ -3602,7 +3583,7 @@
     "            yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
+    "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
    ]
   },
   {
@@ -3621,10 +3602,10 @@
    "id": "fb265379",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:22.322704Z",
-     "iopub.status.busy": "2023-07-26T05:17:22.322434Z",
-     "iopub.status.idle": "2023-07-26T05:17:25.602424Z",
-     "shell.execute_reply": "2023-07-26T05:17:25.601831Z"
+     "iopub.execute_input": "2023-07-26T19:29:52.042970Z",
+     "iopub.status.busy": "2023-07-26T19:29:52.042838Z",
+     "iopub.status.idle": "2023-07-26T19:29:55.163123Z",
+     "shell.execute_reply": "2023-07-26T19:29:55.162863Z"
     }
    },
    "outputs": [
@@ -3632,1014 +3613,1014 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective 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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
        "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                                       (&#x27;ridge&#x27;,\n",
        "                                        ElasticNet(alpha=10000000000.0,\n",
@@ -4694,7 +4675,7 @@
     "grid_r2 = skm.GridSearchCV(pipe, \n",
     "                           param_grid,\n",
     "                           cv=kfold)\n",
-    "grid_r2.fit(X, Y)\n"
+    "grid_r2.fit(X, Y)"
    ]
   },
   {
@@ -4711,12 +4692,11 @@
    "id": "79616f48",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:25.604857Z",
-     "iopub.status.busy": "2023-07-26T05:17:25.604672Z",
-     "iopub.status.idle": "2023-07-26T05:17:25.718682Z",
-     "shell.execute_reply": "2023-07-26T05:17:25.718164Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:29:55.164735Z",
+     "iopub.status.busy": "2023-07-26T19:29:55.164633Z",
+     "iopub.status.idle": "2023-07-26T19:29:55.260392Z",
+     "shell.execute_reply": "2023-07-26T19:29:55.260104Z"
+    }
    },
    "outputs": [
     {
@@ -4736,7 +4716,7 @@
     "            grid_r2.cv_results_['mean_test_score'],\n",
     "            yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);\n"
+    "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);"
    ]
   },
   {
@@ -4760,10 +4740,10 @@
    "id": "11e55883",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:25.721504Z",
-     "iopub.status.busy": "2023-07-26T05:17:25.721335Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.086495Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.085843Z"
+     "iopub.execute_input": "2023-07-26T19:29:55.262198Z",
+     "iopub.status.busy": "2023-07-26T19:29:55.262073Z",
+     "iopub.status.idle": "2023-07-26T19:29:55.617242Z",
+     "shell.execute_reply": "2023-07-26T19:29:55.616916Z"
     }
    },
    "outputs": [
@@ -4771,2014 +4751,2014 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                (&#x27;ridge&#x27;,\n",
        "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
        "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
@@ -6845,7 +6825,7 @@
     "                           cv=kfold)\n",
     "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
     "                         ('ridge', ridgeCV)])\n",
-    "pipeCV.fit(X, Y)\n"
+    "pipeCV.fit(X, Y)"
    ]
   },
   {
@@ -6863,10 +6843,10 @@
    "id": "d107f961",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.090198Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.090011Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.211437Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.210939Z"
+     "iopub.execute_input": "2023-07-26T19:29:55.618934Z",
+     "iopub.status.busy": "2023-07-26T19:29:55.618823Z",
+     "iopub.status.idle": "2023-07-26T19:29:55.720252Z",
+     "shell.execute_reply": "2023-07-26T19:29:55.719855Z"
     }
    },
    "outputs": [
@@ -6890,7 +6870,7 @@
     "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n",
     "ax.set_ylim([50000,250000])\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
+    "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
    ]
   },
   {
@@ -6910,10 +6890,10 @@
    "id": "21d65d1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.214203Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.213975Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.217529Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.216950Z"
+     "iopub.execute_input": "2023-07-26T19:29:55.722001Z",
+     "iopub.status.busy": "2023-07-26T19:29:55.721876Z",
+     "iopub.status.idle": "2023-07-26T19:29:55.724419Z",
+     "shell.execute_reply": "2023-07-26T19:29:55.724119Z"
     }
    },
    "outputs": [
@@ -6929,7 +6909,7 @@
     }
    ],
    "source": [
-    "np.min(tuned_ridge.mse_path_.mean(1))\n"
+    "np.min(tuned_ridge.mse_path_.mean(1))"
    ]
   },
   {
@@ -6949,12 +6929,11 @@
    "id": "11373de9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.219927Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.219801Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.222854Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.222347Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:55.725971Z",
+     "iopub.status.busy": "2023-07-26T19:29:55.725858Z",
+     "iopub.status.idle": "2023-07-26T19:29:55.728165Z",
+     "shell.execute_reply": "2023-07-26T19:29:55.727892Z"
+    }
    },
    "outputs": [
     {
@@ -6973,7 +6952,7 @@
     }
    ],
    "source": [
-    "tuned_ridge.coef_\n"
+    "tuned_ridge.coef_"
    ]
   },
   {
@@ -7014,10 +6993,10 @@
    "id": "72181964",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.225411Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.225192Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.228812Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.228380Z"
+     "iopub.execute_input": "2023-07-26T19:29:55.729917Z",
+     "iopub.status.busy": "2023-07-26T19:29:55.729797Z",
+     "iopub.status.idle": "2023-07-26T19:29:55.732100Z",
+     "shell.execute_reply": "2023-07-26T19:29:55.731823Z"
     }
    },
    "outputs": [],
@@ -7032,7 +7011,7 @@
     "                           l1_ratio=0,\n",
     "                           cv=inner_cv)\n",
     "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('ridge', ridgeCV)]);\n"
+    "                         ('ridge', ridgeCV)]);"
    ]
   },
   {
@@ -7041,2019 +7020,2018 @@
    "id": "4c3054b5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.231904Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.231669Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.556426Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.556096Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:55.733641Z",
+     "iopub.status.busy": "2023-07-26T19:29:55.733524Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.059121Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.058772Z"
+    }
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:614: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n",
       "  model = cd_fast.enet_coordinate_descent_gram(\n",
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:617: 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/islp_freeze_311/lib/python3.11/site-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_39/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:631: ConvergenceWarning: Objective did 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/islp_freeze_311/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did 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"
      ]
     },
@@ -9074,15 +9052,7 @@
     "                             Y,\n",
     "                             cv=outer_valid,\n",
     "                             scoring='neg_mean_squared_error')\n",
-    "-results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf2eba05",
-   "metadata": {},
-   "source": [
-    "    "
+    "-results['test_score']"
    ]
   },
   {
@@ -9107,12 +9077,11 @@
    "id": "72bb0c1d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.560895Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.560717Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.616139Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.615605Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:29:56.061816Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.061667Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.120080Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.119801Z"
+    }
    },
    "outputs": [
     {
@@ -9134,7 +9103,7 @@
     "                         ('lasso', lassoCV)])\n",
     "pipeCV.fit(X, Y)\n",
     "tuned_lasso = pipeCV.named_steps['lasso']\n",
-    "tuned_lasso.alpha_\n"
+    "tuned_lasso.alpha_"
    ]
   },
   {
@@ -9143,12 +9112,11 @@
    "id": "69adc7ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.618605Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.618409Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.629654Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.629177Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:56.121630Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.121547Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.132451Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.132184Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -9158,7 +9126,7 @@
     "                                    n_alphas=100)[:2]\n",
     "soln_path = pd.DataFrame(soln_array.T,\n",
     "                         columns=D.columns,\n",
-    "                         index=-np.log(lambdas))\n"
+    "                         index=-np.log(lambdas))"
    ]
   },
   {
@@ -9177,12 +9145,11 @@
    "id": "2aa75789",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.632157Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.631965Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.833444Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.832719Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:29:56.133919Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.133841Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.301301Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.300888Z"
+    }
    },
    "outputs": [
     {
@@ -9201,7 +9168,7 @@
     "soln_path.plot(ax=ax, legend=False)\n",
     "ax.legend(loc='upper left')\n",
     "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Standardized coefficiients', fontsize=20);\n"
+    "ax.set_ylabel('Standardized coefficiients', fontsize=20);"
    ]
   },
   {
@@ -9220,17 +9187,17 @@
    "id": "e38c9bed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.836589Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.836233Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.841023Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.840686Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.303031Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.302905Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.305467Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.305207Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "114690.73118253548"
+       "114690.73118253727"
       ]
      },
      "execution_count": 46,
@@ -9239,7 +9206,7 @@
     }
    ],
    "source": [
-    "np.min(tuned_lasso.mse_path_.mean(1))\n"
+    "np.min(tuned_lasso.mse_path_.mean(1))"
    ]
   },
   {
@@ -9247,7 +9214,7 @@
    "id": "d091d74c",
    "metadata": {},
    "source": [
-    "Let’s again produce a plot of the cross-validation error.\n"
+    "Let’s again produce a plot of the cross-validation error."
    ]
   },
   {
@@ -9256,10 +9223,10 @@
    "id": "53c42724",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.843812Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.843647Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.968792Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.968349Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.307156Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.307031Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.410756Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.410469Z"
     }
    },
    "outputs": [
@@ -9303,10 +9270,10 @@
    "id": "5f4942ba",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.971271Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.971065Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.974897Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.973908Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.412348Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.412246Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.414506Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.414241Z"
     }
    },
    "outputs": [
@@ -9326,7 +9293,7 @@
     }
    ],
    "source": [
-    "tuned_lasso.coef_\n"
+    "tuned_lasso.coef_"
    ]
   },
   {
@@ -9373,10 +9340,10 @@
    "id": "72a876bb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.977635Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.977456Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.983252Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.982747Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.416085Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.415984Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.420453Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.420184Z"
     }
    },
    "outputs": [
@@ -9397,7 +9364,7 @@
     "pipe = Pipeline([('pca', pca),\n",
     "                 ('linreg', linreg)])\n",
     "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_\n"
+    "pipe.named_steps['linreg'].coef_"
    ]
   },
   {
@@ -9417,10 +9384,10 @@
    "id": "e0e821c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.985960Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.985773Z",
-     "iopub.status.idle": "2023-07-26T05:17:26.991831Z",
-     "shell.execute_reply": "2023-07-26T05:17:26.991247Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.421899Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.421797Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.425543Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.425276Z"
     }
    },
    "outputs": [
@@ -9440,7 +9407,7 @@
     "                 ('pca', pca),\n",
     "                 ('linreg', linreg)])\n",
     "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_\n"
+    "pipe.named_steps['linreg'].coef_"
    ]
   },
   {
@@ -9460,17 +9427,17 @@
    "id": "1ac6886c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:26.994661Z",
-     "iopub.status.busy": "2023-07-26T05:17:26.994482Z",
-     "iopub.status.idle": "2023-07-26T05:17:27.138949Z",
-     "shell.execute_reply": "2023-07-26T05:17:27.138448Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.427046Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.426960Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.547023Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.546651Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
        "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
        "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
        "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
@@ -9503,7 +9470,7 @@
     "                        param_grid,\n",
     "                        cv=kfold,\n",
     "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n"
+    "grid.fit(X, Y)"
    ]
   },
   {
@@ -9520,10 +9487,10 @@
    "id": "5e0c5a96",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:27.141752Z",
-     "iopub.status.busy": "2023-07-26T05:17:27.141555Z",
-     "iopub.status.idle": "2023-07-26T05:17:27.257460Z",
-     "shell.execute_reply": "2023-07-26T05:17:27.256920Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.548687Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.548580Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.648054Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.647754Z"
     }
    },
    "outputs": [
@@ -9547,7 +9514,7 @@
     "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
     "ax.set_xlabel('# principal components', fontsize=20)\n",
     "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);\n"
+    "ax.set_ylim([50000,250000]);"
    ]
   },
   {
@@ -9575,12 +9542,11 @@
    "id": "9df0fb7f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:27.260143Z",
-     "iopub.status.busy": "2023-07-26T05:17:27.259963Z",
-     "iopub.status.idle": "2023-07-26T05:17:27.267101Z",
-     "shell.execute_reply": "2023-07-26T05:17:27.266627Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:29:56.649875Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.649752Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.655697Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.655429Z"
+    }
    },
    "outputs": [
     {
@@ -9601,7 +9567,7 @@
     "                             Y,\n",
     "                             cv=kfold,\n",
     "                             scoring='neg_mean_squared_error')\n",
-    "-cv_null['test_score'].mean()\n"
+    "-cv_null['test_score'].mean()"
    ]
   },
   {
@@ -9621,10 +9587,10 @@
    "id": "458c21a4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:27.269230Z",
-     "iopub.status.busy": "2023-07-26T05:17:27.269087Z",
-     "iopub.status.idle": "2023-07-26T05:17:27.272035Z",
-     "shell.execute_reply": "2023-07-26T05:17:27.271586Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.657281Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.657168Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.659596Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.659278Z"
     }
    },
    "outputs": [
@@ -9640,7 +9606,7 @@
     }
    ],
    "source": [
-    "pipe.named_steps['pca'].explained_variance_ratio_\n"
+    "pipe.named_steps['pca'].explained_variance_ratio_"
    ]
   },
   {
@@ -9653,9 +9619,7 @@
     "that is captured using $M$ principal components. For example, setting\n",
     "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n",
     "By  $M=6$ it increases to\n",
-    "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance.\n",
-    "\n",
-    " "
+    "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance."
    ]
   },
   {
@@ -9665,9 +9629,7 @@
    "source": [
     "### Partial Least Squares\n",
     "Partial least squares (PLS) is implemented in the\n",
-    "`PLSRegression()`  function.\n",
-    "\n",
-    " "
+    "`PLSRegression()`  function."
    ]
   },
   {
@@ -9676,17 +9638,17 @@
    "id": "f49d464e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:27.274642Z",
-     "iopub.status.busy": "2023-07-26T05:17:27.274472Z",
-     "iopub.status.idle": "2023-07-26T05:17:27.279195Z",
-     "shell.execute_reply": "2023-07-26T05:17:27.278730Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.661191Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.661089Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.664609Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.664343Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PLSRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PLSRegression</label><div class=\"sk-toggleable__content\"><pre>PLSRegression()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PLSRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PLSRegression</label><div class=\"sk-toggleable__content\"><pre>PLSRegression()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "PLSRegression()"
@@ -9700,7 +9662,7 @@
    "source": [
     "pls = PLSRegression(n_components=2, \n",
     "                    scale=True)\n",
-    "pls.fit(X, Y)\n"
+    "pls.fit(X, Y)"
    ]
   },
   {
@@ -9718,17 +9680,17 @@
    "id": "8e118b5b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:27.281479Z",
-     "iopub.status.busy": "2023-07-26T05:17:27.281289Z",
-     "iopub.status.idle": "2023-07-26T05:17:27.395617Z",
-     "shell.execute_reply": "2023-07-26T05:17:27.395127Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.666167Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.666047Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.772539Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.772261Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
        "             estimator=PLSRegression(),\n",
        "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
        "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
@@ -9754,7 +9716,7 @@
     "                        param_grid,\n",
     "                        cv=kfold,\n",
     "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n"
+    "grid.fit(X, Y)"
    ]
   },
   {
@@ -9771,10 +9733,10 @@
    "id": "0f98407c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:27.398116Z",
-     "iopub.status.busy": "2023-07-26T05:17:27.397950Z",
-     "iopub.status.idle": "2023-07-26T05:17:27.513229Z",
-     "shell.execute_reply": "2023-07-26T05:17:27.512778Z"
+     "iopub.execute_input": "2023-07-26T19:29:56.774123Z",
+     "iopub.status.busy": "2023-07-26T19:29:56.774035Z",
+     "iopub.status.idle": "2023-07-26T19:29:56.870929Z",
+     "shell.execute_reply": "2023-07-26T19:29:56.870625Z"
     }
    },
    "outputs": [
@@ -9798,7 +9760,7 @@
     "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
     "ax.set_xlabel('# principal components', fontsize=20)\n",
     "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);\n"
+    "ax.set_ylim([50000,250000]);"
    ]
   },
   {
@@ -9807,16 +9769,15 @@
    "metadata": {},
    "source": [
     "CV error is minimized at 12,\n",
-    "though there is little noticable difference between this point and a much lower number like 2 or 3 components.\n",
-    "\n"
+    "though there is little noticable difference between this point and a much lower number like 2 or 3 components."
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -9828,7 +9789,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch7-nonlin-lab.ipynb b/Ch7-nonlin-lab.ipynb
index 3ea5876..0a5b0a7 100644
--- a/Ch7-nonlin-lab.ipynb
+++ b/Ch7-nonlin-lab.ipynb
@@ -5,7 +5,6 @@
    "id": "8d006b12",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 7\n",
     "\n",
     "# Lab: Non-Linear Modeling\n",
@@ -21,10 +20,10 @@
    "id": "77af2dc4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:29.006535Z",
-     "iopub.status.busy": "2023-07-26T05:17:29.006365Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.236882Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.236365Z"
+     "iopub.execute_input": "2023-07-26T19:29:58.548566Z",
+     "iopub.status.busy": "2023-07-26T19:29:58.548460Z",
+     "iopub.status.idle": "2023-07-26T19:29:59.862755Z",
+     "shell.execute_reply": "2023-07-26T19:29:59.862359Z"
     }
    },
    "outputs": [],
@@ -55,10 +54,10 @@
    "id": "b57d1d51",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.240347Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.240127Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.254879Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.254420Z"
+     "iopub.execute_input": "2023-07-26T19:29:59.865138Z",
+     "iopub.status.busy": "2023-07-26T19:29:59.864879Z",
+     "iopub.status.idle": "2023-07-26T19:29:59.880281Z",
+     "shell.execute_reply": "2023-07-26T19:29:59.879996Z"
     }
    },
    "outputs": [],
@@ -75,7 +74,7 @@
     "from ISLP.pygam import (approx_lam,\n",
     "                        degrees_of_freedom,\n",
     "                        plot as plot_gam,\n",
-    "                        anova as anova_gam)\n"
+    "                        anova as anova_gam)"
    ]
   },
   {
@@ -94,17 +93,17 @@
    "id": "65f0e562",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.257519Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.257098Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.265239Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.264877Z"
+     "iopub.execute_input": "2023-07-26T19:29:59.881915Z",
+     "iopub.status.busy": "2023-07-26T19:29:59.881815Z",
+     "iopub.status.idle": "2023-07-26T19:29:59.890148Z",
+     "shell.execute_reply": "2023-07-26T19:29:59.889891Z"
     }
    },
    "outputs": [],
    "source": [
     "Wage = load_data('Wage')\n",
     "y = Wage['wage']\n",
-    "age = Wage['age']\n"
+    "age = Wage['age']"
    ]
   },
   {
@@ -124,12 +123,11 @@
    "id": "a391fae6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.267640Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.267444Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.380537Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.378736Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:29:59.892002Z",
+     "iopub.status.busy": "2023-07-26T19:29:59.891898Z",
+     "iopub.status.idle": "2023-07-26T19:29:59.990382Z",
+     "shell.execute_reply": "2023-07-26T19:29:59.985457Z"
+    }
    },
    "outputs": [
     {
@@ -216,7 +214,7 @@
    "source": [
     "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n",
     "M = sm.OLS(y, poly_age.transform(Wage)).fit()\n",
-    "summarize(M)\n"
+    "summarize(M)"
    ]
   },
   {
@@ -238,10 +236,7 @@
     "`Wage`. This recomputes and stores as attributes any parameters needed by `Poly()`\n",
     "on the training data, and these will be used on all subsequent\n",
     "evaluations of the `transform()` method. For example, it is used\n",
-    "on the second line, as well as in the plotting function developed below.\n",
-    "\n",
-    "\n",
-    "    \n"
+    "on the second line, as well as in the plotting function developed below."
    ]
   },
   {
@@ -259,10 +254,10 @@
    "id": "d672f40e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.388859Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.387288Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.401783Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.398614Z"
+     "iopub.execute_input": "2023-07-26T19:29:59.995236Z",
+     "iopub.status.busy": "2023-07-26T19:29:59.994589Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.021871Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.000494Z"
     }
    },
    "outputs": [],
@@ -270,7 +265,7 @@
     "age_grid = np.linspace(age.min(),\n",
     "                       age.max(),\n",
     "                       100)\n",
-    "age_df = pd.DataFrame({'age': age_grid})\n"
+    "age_df = pd.DataFrame({'age': age_grid})"
    ]
   },
   {
@@ -285,7 +280,7 @@
     "transform), as well as a grid of `age` values. The function\n",
     "produces a fitted curve as well as 95% confidence bands. By using\n",
     "an argument for `basis` we can produce and plot the results with several different\n",
-    "transforms, such as the splines we will see shortly. "
+    "transforms, such as the splines we will see shortly."
    ]
   },
   {
@@ -294,12 +289,11 @@
    "id": "56174265",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.414085Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.411883Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.434639Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.432466Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.027847Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.027510Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.034738Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.033444Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -325,7 +319,7 @@
     "    ax.set_title(title, fontsize=20)\n",
     "    ax.set_xlabel('Age', fontsize=20)\n",
     "    ax.set_ylabel('Wage', fontsize=20);\n",
-    "    return ax\n"
+    "    return ax"
    ]
   },
   {
@@ -351,12 +345,11 @@
    "id": "1a10422f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.447621Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.446550Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.730675Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.730133Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.043053Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.042526Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.265424Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.262674Z"
+    }
    },
    "outputs": [
     {
@@ -373,17 +366,7 @@
    "source": [
     "plot_wage_fit(age_df, \n",
     "              poly_age,\n",
-    "              'Degree-4 Polynomial');\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c5aa81b",
-   "metadata": {},
-   "source": [
-    " \n",
-    "\n",
-    "    "
+    "              'Degree-4 Polynomial');"
    ]
   },
   {
@@ -420,10 +403,10 @@
    "id": "8105e672",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.732769Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.732623Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.781558Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.779955Z"
+     "iopub.execute_input": "2023-07-26T19:30:00.289863Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.287444Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.334859Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.334154Z"
     }
    },
    "outputs": [
@@ -525,7 +508,7 @@
     "          for d in range(1, 6)]\n",
     "Xs = [model.fit_transform(Wage) for model in models]\n",
     "anova_lm(*[sm.OLS(y, X_).fit()\n",
-    "           for X_ in Xs])\n"
+    "           for X_ in Xs])"
    ]
   },
   {
@@ -560,12 +543,11 @@
    "id": "095fca7f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.790773Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.789916Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.829956Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.827755Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:00.340159Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.339913Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.360016Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.358938Z"
+    }
    },
    "outputs": [
     {
@@ -650,7 +632,7 @@
     }
    ],
    "source": [
-    "summarize(M)\n"
+    "summarize(M)"
    ]
   },
   {
@@ -660,7 +642,7 @@
    "source": [
     "Notice that the p-values are the same, and in fact the square of\n",
     "the  t-statistics are equal to the F-statistics from the\n",
-    "`anova_lm()`  function;  for example: "
+    "`anova_lm()`  function;  for example:"
    ]
   },
   {
@@ -669,12 +651,11 @@
    "id": "0b576607",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.839811Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.837704Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.856244Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.854399Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:00.366939Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.366658Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.373285Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.371327Z"
+    }
    },
    "outputs": [
     {
@@ -689,7 +670,7 @@
     }
    ],
    "source": [
-    "(-11.983)**2\n"
+    "(-11.983)**2"
    ]
   },
   {
@@ -710,12 +691,11 @@
    "id": "7242df77",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.866948Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.865892Z",
-     "iopub.status.idle": "2023-07-26T05:17:30.922189Z",
-     "shell.execute_reply": "2023-07-26T05:17:30.920195Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:00.377586Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.377243Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.414526Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.413561Z"
+    }
    },
    "outputs": [
     {
@@ -796,7 +776,7 @@
     "          for d in range(1, 4)]\n",
     "XEs = [model.fit_transform(Wage)\n",
     "       for model in models]\n",
-    "anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])\n"
+    "anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])"
    ]
   },
   {
@@ -820,12 +800,11 @@
    "id": "c7972aea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:30.932685Z",
-     "iopub.status.busy": "2023-07-26T05:17:30.932050Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.040926Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.003239Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:00.419224Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.418841Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.450847Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.449742Z"
+    }
    },
    "outputs": [
     {
@@ -916,7 +895,7 @@
     "             X,\n",
     "             family=sm.families.Binomial())\n",
     "B = glm.fit()\n",
-    "summarize(B)\n"
+    "summarize(B)"
    ]
   },
   {
@@ -933,17 +912,17 @@
    "id": "84f172f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.067292Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.066686Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.149946Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.109686Z"
+     "iopub.execute_input": "2023-07-26T19:30:00.456457Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.456134Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.464946Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.464036Z"
     }
    },
    "outputs": [],
    "source": [
     "newX = poly_age.transform(age_df)\n",
     "preds = B.get_prediction(newX)\n",
-    "bands = preds.conf_int(alpha=0.05)\n"
+    "bands = preds.conf_int(alpha=0.05)"
    ]
   },
   {
@@ -960,12 +939,11 @@
    "id": "fcf376bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.174590Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.158361Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.302984Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.302329Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.468703Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.468445Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.618767Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.617143Z"
+    }
    },
    "outputs": [
     {
@@ -995,7 +973,7 @@
     "ax.set_title('Degree-4 Polynomial', fontsize=20)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylim([0,0.2])\n",
-    "ax.set_ylabel('P(Wage > 250)', fontsize=20);\n"
+    "ax.set_ylabel('P(Wage > 250)', fontsize=20);"
    ]
   },
   {
@@ -1025,12 +1003,11 @@
    "id": "c4d00900",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.305474Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.305329Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.336068Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.334266Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:00.624262Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.623922Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.655951Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.655214Z"
+    }
    },
    "outputs": [
     {
@@ -1108,7 +1085,7 @@
    ],
    "source": [
     "cut_age = pd.qcut(age, 4)\n",
-    "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())\n"
+    "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())"
    ]
   },
   {
@@ -1154,12 +1131,11 @@
    "id": "d3817102",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.345711Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.343152Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.444719Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.422743Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.660378Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.660082Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.674317Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.673635Z"
+    }
    },
    "outputs": [
     {
@@ -1176,7 +1152,7 @@
    "source": [
     "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n",
     "bs_age = bs_.transform(age)\n",
-    "bs_age.shape\n"
+    "bs_age.shape"
    ]
   },
   {
@@ -1188,7 +1164,7 @@
     "We can form this same matrix using the `bs()` object,\n",
     "which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section 7.8.1.\n",
     "\n",
-    "We now fit a cubic spline model to the `Wage`  data. "
+    "We now fit a cubic spline model to the `Wage`  data."
    ]
   },
   {
@@ -1197,12 +1173,11 @@
    "id": "c06c0f27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.504387Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.502827Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.545517Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.543597Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.678223Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.677950Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.699133Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.697370Z"
+    }
    },
    "outputs": [
     {
@@ -1306,7 +1281,7 @@
     "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n",
     "Xbs = bs_age.fit_transform(Wage)\n",
     "M = sm.OLS(y, Xbs).fit()\n",
-    "summarize(M)\n"
+    "summarize(M)"
    ]
   },
   {
@@ -1323,10 +1298,10 @@
    "id": "8cb32db0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.554111Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.553059Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.605969Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.601942Z"
+     "iopub.execute_input": "2023-07-26T19:30:00.702994Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.702578Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.733650Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.732960Z"
     }
    },
    "outputs": [
@@ -1433,7 +1408,7 @@
     "                name='bs(age)')])\n",
     "Xbs = bs_age.fit_transform(Wage)\n",
     "M = sm.OLS(y, Xbs).fit()\n",
-    "summarize(M)\n"
+    "summarize(M)"
    ]
   },
   {
@@ -1459,12 +1434,11 @@
    "id": "f26b715f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.613882Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.613189Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.632577Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.631063Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.738238Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.736892Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.746487Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.744099Z"
+    }
    },
    "outputs": [
     {
@@ -1479,7 +1453,7 @@
     }
    ],
    "source": [
-    "BSpline(df=6).fit(age).internal_knots_\n"
+    "BSpline(df=6).fit(age).internal_knots_"
    ]
   },
   {
@@ -1495,7 +1469,7 @@
     "When using B-splines we need not limit ourselves to cubic polynomials\n",
     "(i.e. `degree=3`). For instance, using `degree=0` results\n",
     "in piecewise constant functions, as in our example with\n",
-    "`pd.qcut()` above.\n"
+    "`pd.qcut()` above."
    ]
   },
   {
@@ -1504,12 +1478,11 @@
    "id": "d6109816",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.640827Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.640229Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.697215Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.695639Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.750506Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.749854Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.772527Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.771748Z"
+    }
    },
    "outputs": [
     {
@@ -1590,7 +1563,7 @@
     "                 df=3, \n",
     "                 degree=0)]).fit(Wage)\n",
     "Xbs0 = bs_age0.transform(Wage)\n",
-    "summarize(sm.OLS(y, Xbs0).fit())\n"
+    "summarize(sm.OLS(y, Xbs0).fit())"
    ]
   },
   {
@@ -1610,16 +1583,7 @@
     "of ones, so the second, third and fourth coefficients are increments\n",
     "for those bins. Why is the sum not exactly the same? It turns out that\n",
     "the `qcut()` uses $\\leq$, while `bs()` uses $<$ when\n",
-    "deciding bin membership.\n",
-    "\n",
-    "\n",
-    "    \n",
-    " \n",
-    "\n",
-    " \n",
-    "\n",
-    "    \n",
-    " "
+    "deciding bin membership."
    ]
   },
   {
@@ -1638,12 +1602,11 @@
    "id": "7c73a3da",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.706944Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.705664Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.757111Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.754514Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:00.776268Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.775697Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.802509Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.800182Z"
+    }
    },
    "outputs": [
     {
@@ -1755,10 +1718,10 @@
    "id": "d73789b4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.766336Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.764642Z",
-     "iopub.status.idle": "2023-07-26T05:17:31.949222Z",
-     "shell.execute_reply": "2023-07-26T05:17:31.947680Z"
+     "iopub.execute_input": "2023-07-26T19:30:00.807452Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.806662Z",
+     "iopub.status.idle": "2023-07-26T19:30:00.972430Z",
+     "shell.execute_reply": "2023-07-26T19:30:00.970608Z"
     }
    },
    "outputs": [
@@ -1776,7 +1739,7 @@
    "source": [
     "plot_wage_fit(age_df,\n",
     "              ns_age,\n",
-    "              'Natural spline, df=5');\n"
+    "              'Natural spline, df=5');"
    ]
   },
   {
@@ -1803,10 +1766,10 @@
    "id": "3e70b87d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:31.962335Z",
-     "iopub.status.busy": "2023-07-26T05:17:31.958770Z",
-     "iopub.status.idle": "2023-07-26T05:17:32.190885Z",
-     "shell.execute_reply": "2023-07-26T05:17:32.188959Z"
+     "iopub.execute_input": "2023-07-26T19:30:00.976289Z",
+     "iopub.status.busy": "2023-07-26T19:30:00.975775Z",
+     "iopub.status.idle": "2023-07-26T19:30:01.018838Z",
+     "shell.execute_reply": "2023-07-26T19:30:01.018171Z"
     }
    },
    "outputs": [
@@ -1826,7 +1789,7 @@
    "source": [
     "X_age = np.asarray(age).reshape((-1,1))\n",
     "gam = LinearGAM(s_gam(0, lam=0.6))\n",
-    "gam.fit(X_age, y)\n"
+    "gam.fit(X_age, y)"
    ]
   },
   {
@@ -1849,10 +1812,10 @@
    "id": "efe03b12",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:32.212724Z",
-     "iopub.status.busy": "2023-07-26T05:17:32.212362Z",
-     "iopub.status.idle": "2023-07-26T05:17:33.141120Z",
-     "shell.execute_reply": "2023-07-26T05:17:33.140497Z"
+     "iopub.execute_input": "2023-07-26T19:30:01.023545Z",
+     "iopub.status.busy": "2023-07-26T19:30:01.023263Z",
+     "iopub.status.idle": "2023-07-26T19:30:01.335107Z",
+     "shell.execute_reply": "2023-07-26T19:30:01.334312Z"
     }
    },
    "outputs": [
@@ -1878,7 +1841,7 @@
     "            linewidth=3)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='$\\lambda$');\n"
+    "ax.legend(title='$\\lambda$');"
    ]
   },
   {
@@ -1895,10 +1858,10 @@
    "id": "acff2af2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:33.143621Z",
-     "iopub.status.busy": "2023-07-26T05:17:33.143494Z",
-     "iopub.status.idle": "2023-07-26T05:17:34.781067Z",
-     "shell.execute_reply": "2023-07-26T05:17:34.758430Z"
+     "iopub.execute_input": "2023-07-26T19:30:01.340045Z",
+     "iopub.status.busy": "2023-07-26T19:30:01.339738Z",
+     "iopub.status.idle": "2023-07-26T19:30:01.717518Z",
+     "shell.execute_reply": "2023-07-26T19:30:01.716788Z"
     }
    },
    "outputs": [
@@ -1906,7 +1869,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100% (11 of 11) |########################| Elapsed Time: 0:00:01 Time:  0:00:01\n"
+      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
      ]
     },
     {
@@ -1928,7 +1891,7 @@
     "        label='Grid search',\n",
     "        linewidth=4)\n",
     "ax.legend()\n",
-    "fig\n"
+    "fig"
    ]
   },
   {
@@ -1950,18 +1913,17 @@
    "id": "a2d25550",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:34.825629Z",
-     "iopub.status.busy": "2023-07-26T05:17:34.825425Z",
-     "iopub.status.idle": "2023-07-26T05:17:35.011756Z",
-     "shell.execute_reply": "2023-07-26T05:17:35.006694Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:01.722241Z",
+     "iopub.status.busy": "2023-07-26T19:30:01.721638Z",
+     "iopub.status.idle": "2023-07-26T19:30:01.742018Z",
+     "shell.execute_reply": "2023-07-26T19:30:01.741073Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4.000000100001664"
+       "4.000000100001163"
       ]
      },
      "execution_count": 26,
@@ -1973,7 +1935,7 @@
     "age_term = gam.terms[0]\n",
     "lam_4 = approx_lam(X_age, age_term, 4)\n",
     "age_term.lam = lam_4\n",
-    "degrees_of_freedom(X_age, age_term)\n"
+    "degrees_of_freedom(X_age, age_term)"
    ]
   },
   {
@@ -1992,16 +1954,16 @@
    "id": "bc6322de",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:35.024537Z",
-     "iopub.status.busy": "2023-07-26T05:17:35.022461Z",
-     "iopub.status.idle": "2023-07-26T05:17:35.863008Z",
-     "shell.execute_reply": "2023-07-26T05:17:35.862422Z"
+     "iopub.execute_input": "2023-07-26T19:30:01.746687Z",
+     "iopub.status.busy": "2023-07-26T19:30:01.745367Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.063975Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.061953Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -2026,7 +1988,7 @@
     "            linewidth=4)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='Degrees of freedom');\n"
+    "ax.legend(title='Degrees of freedom');"
    ]
   },
   {
@@ -2054,12 +2016,11 @@
    "id": "703d2570",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:35.865454Z",
-     "iopub.status.busy": "2023-07-26T05:17:35.865140Z",
-     "iopub.status.idle": "2023-07-26T05:17:35.892627Z",
-     "shell.execute_reply": "2023-07-26T05:17:35.889738Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:02.068387Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.068151Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.092605Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.091688Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -2069,7 +2030,7 @@
     "      ns_year.transform(Wage['year']),\n",
     "      pd.get_dummies(Wage['education']).values]\n",
     "X_bh = np.hstack(Xs)\n",
-    "gam_bh = sm.OLS(y, X_bh).fit()\n"
+    "gam_bh = sm.OLS(y, X_bh).fit()"
    ]
   },
   {
@@ -2093,12 +2054,11 @@
    "id": "766a7b1f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:35.908556Z",
-     "iopub.status.busy": "2023-07-26T05:17:35.902581Z",
-     "iopub.status.idle": "2023-07-26T05:17:36.057026Z",
-     "shell.execute_reply": "2023-07-26T05:17:36.055296Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:02.100057Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.099286Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.283723Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.283010Z"
+    }
    },
    "outputs": [
     {
@@ -2131,7 +2091,7 @@
     "ax.plot(age_grid, bounds_age[:,1], 'r--', linewidth=3)\n",
     "ax.set_xlabel('Age')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of age on wage', fontsize=20);\n"
+    "ax.set_title('Partial dependence of age on wage', fontsize=20);"
    ]
   },
   {
@@ -2156,10 +2116,10 @@
    "id": "2a9ed841",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:36.068318Z",
-     "iopub.status.busy": "2023-07-26T05:17:36.067032Z",
-     "iopub.status.idle": "2023-07-26T05:17:36.214624Z",
-     "shell.execute_reply": "2023-07-26T05:17:36.214052Z"
+     "iopub.execute_input": "2023-07-26T19:30:02.289076Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.288796Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.421352Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.421033Z"
     }
    },
    "outputs": [
@@ -2194,7 +2154,7 @@
     "ax.plot(year_grid, bounds_year[:,1], 'r--', linewidth=3)\n",
     "ax.set_xlabel('Year')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of year on wage', fontsize=20);\n"
+    "ax.set_title('Partial dependence of year on wage', fontsize=20);"
    ]
   },
   {
@@ -2217,12 +2177,11 @@
    "id": "ccf068b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:36.216725Z",
-     "iopub.status.busy": "2023-07-26T05:17:36.216587Z",
-     "iopub.status.idle": "2023-07-26T05:17:36.383722Z",
-     "shell.execute_reply": "2023-07-26T05:17:36.379206Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:02.423254Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.422983Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.444181Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.443375Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -2232,7 +2191,7 @@
     "Xgam = np.column_stack([age,\n",
     "                        Wage['year'],\n",
     "                        Wage['education'].cat.codes])\n",
-    "gam_full = gam_full.fit(Xgam, y)\n"
+    "gam_full = gam_full.fit(Xgam, y)"
    ]
   },
   {
@@ -2254,10 +2213,10 @@
    "id": "38b719f1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:36.404487Z",
-     "iopub.status.busy": "2023-07-26T05:17:36.401365Z",
-     "iopub.status.idle": "2023-07-26T05:17:36.620141Z",
-     "shell.execute_reply": "2023-07-26T05:17:36.619756Z"
+     "iopub.execute_input": "2023-07-26T19:30:02.448633Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.448241Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.581291Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.579631Z"
     }
    },
    "outputs": [
@@ -2277,7 +2236,7 @@
     "plot_gam(gam_full, 0, ax=ax)\n",
     "ax.set_xlabel('Age')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);\n"
+    "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);"
    ]
   },
   {
@@ -2297,12 +2256,11 @@
    "id": "02142f6e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:36.622262Z",
-     "iopub.status.busy": "2023-07-26T05:17:36.622071Z",
-     "iopub.status.idle": "2023-07-26T05:17:36.728386Z",
-     "shell.execute_reply": "2023-07-26T05:17:36.725088Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:02.585698Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.585237Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.648951Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.647958Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -2310,7 +2268,7 @@
     "age_term.lam = approx_lam(Xgam, age_term, df=4+1)\n",
     "year_term = gam_full.terms[1]\n",
     "year_term.lam = approx_lam(Xgam, year_term, df=4+1)\n",
-    "gam_full = gam_full.fit(Xgam, y)\n"
+    "gam_full = gam_full.fit(Xgam, y)"
    ]
   },
   {
@@ -2330,12 +2288,11 @@
    "id": "94587b05",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:36.738456Z",
-     "iopub.status.busy": "2023-07-26T05:17:36.737408Z",
-     "iopub.status.idle": "2023-07-26T05:17:36.927722Z",
-     "shell.execute_reply": "2023-07-26T05:17:36.917522Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:02.653194Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.652831Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.808659Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.807342Z"
+    }
    },
    "outputs": [
     {
@@ -2350,7 +2307,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -2366,7 +2323,7 @@
     "         ax=ax)\n",
     "ax.set_xlabel('Year')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of year on wage', fontsize=20)\n"
+    "ax.set_title('Partial dependence of year on wage', fontsize=20)"
    ]
   },
   {
@@ -2374,7 +2331,7 @@
    "id": "5b373d5c",
    "metadata": {},
    "source": [
-    "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable. "
+    "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable."
    ]
   },
   {
@@ -2383,10 +2340,10 @@
    "id": "bba4c757",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:36.975410Z",
-     "iopub.status.busy": "2023-07-26T05:17:36.975007Z",
-     "iopub.status.idle": "2023-07-26T05:17:37.089224Z",
-     "shell.execute_reply": "2023-07-26T05:17:37.088696Z"
+     "iopub.execute_input": "2023-07-26T19:30:02.813190Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.812200Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.932249Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.931965Z"
     }
    },
    "outputs": [
@@ -2408,7 +2365,7 @@
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of wage on education',\n",
     "             fontsize=20);\n",
-    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
+    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);"
    ]
   },
   {
@@ -2430,10 +2387,10 @@
    "id": "32368085",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:37.091963Z",
-     "iopub.status.busy": "2023-07-26T05:17:37.091763Z",
-     "iopub.status.idle": "2023-07-26T05:17:37.181347Z",
-     "shell.execute_reply": "2023-07-26T05:17:37.177854Z"
+     "iopub.execute_input": "2023-07-26T19:30:02.933978Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.933859Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.968112Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.967368Z"
     }
    },
    "outputs": [
@@ -2456,7 +2413,7 @@
     "gam_linear = LinearGAM(age_term +\n",
     "                       l_gam(1, lam=0) +\n",
     "                       f_gam(2, lam=0))\n",
-    "gam_linear.fit(Xgam, y)\n"
+    "gam_linear.fit(Xgam, y)"
    ]
   },
   {
@@ -2477,12 +2434,11 @@
    "id": "e7ba9957",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:37.190026Z",
-     "iopub.status.busy": "2023-07-26T05:17:37.188398Z",
-     "iopub.status.idle": "2023-07-26T05:17:37.218825Z",
-     "shell.execute_reply": "2023-07-26T05:17:37.206670Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:02.973107Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.972753Z",
+     "iopub.status.idle": "2023-07-26T19:30:02.985210Z",
+     "shell.execute_reply": "2023-07-26T19:30:02.984394Z"
+    }
    },
    "outputs": [
     {
@@ -2559,7 +2515,7 @@
     }
    ],
    "source": [
-    "anova_gam(gam_0, gam_linear, gam_full)\n"
+    "anova_gam(gam_0, gam_linear, gam_full)"
    ]
   },
   {
@@ -2585,10 +2541,10 @@
    "id": "ffc0099a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:37.237108Z",
-     "iopub.status.busy": "2023-07-26T05:17:37.233509Z",
-     "iopub.status.idle": "2023-07-26T05:17:37.451340Z",
-     "shell.execute_reply": "2023-07-26T05:17:37.449175Z"
+     "iopub.execute_input": "2023-07-26T19:30:02.990369Z",
+     "iopub.status.busy": "2023-07-26T19:30:02.990083Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.049397Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.048695Z"
     }
    },
    "outputs": [
@@ -2695,10 +2651,10 @@
    "id": "08026a6b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:37.465361Z",
-     "iopub.status.busy": "2023-07-26T05:17:37.464408Z",
-     "iopub.status.idle": "2023-07-26T05:17:37.476379Z",
-     "shell.execute_reply": "2023-07-26T05:17:37.473563Z"
+     "iopub.execute_input": "2023-07-26T19:30:03.055222Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.053410Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.065149Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.060566Z"
     }
    },
    "outputs": [
@@ -2737,7 +2693,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_69087/2135516388.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_6131/3870570873.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",
@@ -2749,7 +2705,7 @@
     }
    ],
    "source": [
-    "gam_full.summary()\n"
+    "gam_full.summary()"
    ]
   },
   {
@@ -2768,15 +2724,15 @@
    "id": "9191d615",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:37.488339Z",
-     "iopub.status.busy": "2023-07-26T05:17:37.487573Z",
-     "iopub.status.idle": "2023-07-26T05:17:37.520050Z",
-     "shell.execute_reply": "2023-07-26T05:17:37.516172Z"
+     "iopub.execute_input": "2023-07-26T19:30:03.069313Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.069065Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.085522Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.084690Z"
     }
    },
    "outputs": [],
    "source": [
-    "Yhat = gam_full.predict(Xgam)\n"
+    "Yhat = gam_full.predict(Xgam)"
    ]
   },
   {
@@ -2794,12 +2750,11 @@
    "id": "92007a5f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:37.533259Z",
-     "iopub.status.busy": "2023-07-26T05:17:37.531878Z",
-     "iopub.status.idle": "2023-07-26T05:17:38.910096Z",
-     "shell.execute_reply": "2023-07-26T05:17:38.906703Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:03.090132Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.089881Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.201833Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.201245Z"
+    }
    },
    "outputs": [
     {
@@ -2819,7 +2774,7 @@
     "gam_logit = LogisticGAM(age_term + \n",
     "                        l_gam(1, lam=0) +\n",
     "                        f_gam(2, lam=0))\n",
-    "gam_logit.fit(Xgam, high_earn)\n"
+    "gam_logit.fit(Xgam, high_earn)"
    ]
   },
   {
@@ -2828,12 +2783,11 @@
    "id": "4dd6aa2f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:38.924790Z",
-     "iopub.status.busy": "2023-07-26T05:17:38.922174Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.053438Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.047514Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:03.204948Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.204718Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.405651Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.404596Z"
+    }
    },
    "outputs": [
     {
@@ -2854,7 +2808,7 @@
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of wage on education',\n",
     "             fontsize=20);\n",
-    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
+    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);"
    ]
   },
   {
@@ -2872,12 +2826,11 @@
    "id": "5a6e8754",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:39.066631Z",
-     "iopub.status.busy": "2023-07-26T05:17:39.064606Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.115103Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.111772Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:03.410803Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.408822Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.436206Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.434925Z"
+    }
    },
    "outputs": [
     {
@@ -2955,7 +2908,7 @@
     }
    ],
    "source": [
-    "pd.crosstab(Wage['high_earn'], Wage['education'])\n"
+    "pd.crosstab(Wage['high_earn'], Wage['education'])"
    ]
   },
   {
@@ -2972,7 +2925,7 @@
     "we could subset the model matrix, though this will not remove the\n",
     "column from `Xgam`. While we can deduce which column corresponds\n",
     "to this feature, for reproducibility’s sake we reform the model matrix\n",
-    "on this smaller subset.\n"
+    "on this smaller subset."
    ]
   },
   {
@@ -2981,12 +2934,11 @@
    "id": "c92b60be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:39.164808Z",
-     "iopub.status.busy": "2023-07-26T05:17:39.164544Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.168909Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.168184Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:03.442427Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.441250Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.449300Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.448611Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -2995,7 +2947,7 @@
     "Xgam_ = np.column_stack([Wage_['age'],\n",
     "                         Wage_['year'],\n",
     "                         Wage_['education'].cat.codes-1])\n",
-    "high_earn_ = Wage_['high_earn']\n"
+    "high_earn_ = Wage_['high_earn']"
    ]
   },
   {
@@ -3015,12 +2967,11 @@
    "id": "e525e8d0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:39.173520Z",
-     "iopub.status.busy": "2023-07-26T05:17:39.173179Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.334235Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.329778Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:03.452617Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.452392Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.500282Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.499537Z"
+    }
    },
    "outputs": [
     {
@@ -3040,7 +2991,7 @@
     "gam_logit_ = LogisticGAM(age_term +\n",
     "                         year_term +\n",
     "                         f_gam(2, lam=0))\n",
-    "gam_logit_.fit(Xgam_, high_earn_)\n"
+    "gam_logit_.fit(Xgam_, high_earn_)"
    ]
   },
   {
@@ -3058,10 +3009,10 @@
    "id": "c3e66cf9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:39.345416Z",
-     "iopub.status.busy": "2023-07-26T05:17:39.344660Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.468357Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.465567Z"
+     "iopub.execute_input": "2023-07-26T19:30:03.505975Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.505674Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.667572Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.666611Z"
     }
    },
    "outputs": [
@@ -3083,7 +3034,7 @@
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of high earner status on education', fontsize=20);\n",
     "ax.set_xticklabels(Wage['education'].cat.categories[1:],\n",
-    "                   fontsize=8);\n"
+    "                   fontsize=8);"
    ]
   },
   {
@@ -3092,16 +3043,16 @@
    "id": "fd924348",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:39.479166Z",
-     "iopub.status.busy": "2023-07-26T05:17:39.477744Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.605116Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.604574Z"
+     "iopub.execute_input": "2023-07-26T19:30:03.672481Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.671964Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.790367Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.790071Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -3116,7 +3067,7 @@
     "ax.set_xlabel('Year')\n",
     "ax.set_ylabel('Effect on wage')\n",
     "ax.set_title('Partial dependence of high earner status on year',\n",
-    "             fontsize=20);\n"
+    "             fontsize=20);"
    ]
   },
   {
@@ -3125,17 +3076,16 @@
    "id": "2d3ec90a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:39.607454Z",
-     "iopub.status.busy": "2023-07-26T05:17:39.607274Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.737422Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.736830Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:03.798402Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.798269Z",
+     "iopub.status.idle": "2023-07-26T19:30:03.913307Z",
+     "shell.execute_reply": "2023-07-26T19:30:03.913014Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -3149,7 +3099,7 @@
     "ax = plot_gam(gam_logit_, 0)\n",
     "ax.set_xlabel('Age')\n",
     "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of high earner status on age', fontsize=20);\n"
+    "ax.set_title('Partial dependence of high earner status on age', fontsize=20);"
    ]
   },
   {
@@ -3173,12 +3123,11 @@
    "id": "4f2bc0eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:39.740507Z",
-     "iopub.status.busy": "2023-07-26T05:17:39.740171Z",
-     "iopub.status.idle": "2023-07-26T05:17:39.902495Z",
-     "shell.execute_reply": "2023-07-26T05:17:39.902127Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:03.915002Z",
+     "iopub.status.busy": "2023-07-26T19:30:03.914883Z",
+     "iopub.status.idle": "2023-07-26T19:30:04.060293Z",
+     "shell.execute_reply": "2023-07-26T19:30:04.059975Z"
+    }
    },
    "outputs": [
     {
@@ -3207,24 +3156,15 @@
     "            linewidth=4)\n",
     "ax.set_xlabel('Age', fontsize=20)\n",
     "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='span', fontsize=15);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ce87f4b",
-   "metadata": {},
-   "source": [
-    "  \n",
-    " \n"
+    "ax.legend(title='span', fontsize=15);"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3236,7 +3176,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch8-baggboost-lab.ipynb b/Ch8-baggboost-lab.ipynb
index f26db43..95d0e81 100644
--- a/Ch8-baggboost-lab.ipynb
+++ b/Ch8-baggboost-lab.ipynb
@@ -5,9 +5,7 @@
    "id": "7b0242c6",
    "metadata": {},
    "source": [
-    "\n",
-    "# Chapter 8\n",
-    "\n"
+    "# Chapter 8"
    ]
   },
   {
@@ -26,12 +24,11 @@
    "id": "4cc7120f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:41.271445Z",
-     "iopub.status.busy": "2023-07-26T05:17:41.271226Z",
-     "iopub.status.idle": "2023-07-26T05:17:42.756473Z",
-     "shell.execute_reply": "2023-07-26T05:17:42.756027Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:05.371862Z",
+     "iopub.status.busy": "2023-07-26T19:30:05.371761Z",
+     "iopub.status.idle": "2023-07-26T19:30:06.605449Z",
+     "shell.execute_reply": "2023-07-26T19:30:06.604954Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -41,7 +38,7 @@
     "from statsmodels.datasets import get_rdataset\n",
     "import sklearn.model_selection as skm\n",
     "from ISLP import load_data, confusion_table\n",
-    "from ISLP.models import ModelSpec as MS\n"
+    "from ISLP.models import ModelSpec as MS"
    ]
   },
   {
@@ -59,12 +56,11 @@
    "id": "864fa15e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:42.759051Z",
-     "iopub.status.busy": "2023-07-26T05:17:42.758860Z",
-     "iopub.status.idle": "2023-07-26T05:17:42.832228Z",
-     "shell.execute_reply": "2023-07-26T05:17:42.831597Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:06.609335Z",
+     "iopub.status.busy": "2023-07-26T19:30:06.609115Z",
+     "iopub.status.idle": "2023-07-26T19:30:07.729911Z",
+     "shell.execute_reply": "2023-07-26T19:30:07.729621Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -77,7 +73,7 @@
     "from sklearn.ensemble import \\\n",
     "     (RandomForestRegressor as RF,\n",
     "      GradientBoostingRegressor as GBR)\n",
-    "from ISLP.bart import BART\n"
+    "from ISLP.bart import BART"
    ]
   },
   {
@@ -107,10 +103,10 @@
    "id": "bfb4c83b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:42.836883Z",
-     "iopub.status.busy": "2023-07-26T05:17:42.836681Z",
-     "iopub.status.idle": "2023-07-26T05:17:42.843170Z",
-     "shell.execute_reply": "2023-07-26T05:17:42.842647Z"
+     "iopub.execute_input": "2023-07-26T19:30:07.731837Z",
+     "iopub.status.busy": "2023-07-26T19:30:07.731719Z",
+     "iopub.status.idle": "2023-07-26T19:30:07.737155Z",
+     "shell.execute_reply": "2023-07-26T19:30:07.736878Z"
     }
    },
    "outputs": [],
@@ -118,7 +114,7 @@
     "Carseats = load_data('Carseats')\n",
     "High = np.where(Carseats.Sales > 8,\n",
     "                \"Yes\",\n",
-    "                \"No\")\n"
+    "                \"No\")"
    ]
   },
   {
@@ -129,7 +125,7 @@
     "We now use `DecisionTreeClassifier()`  to fit a classification tree in\n",
     "order to predict `High` using all variables but `Sales`.\n",
     "To do so, we must form a model matrix as we did when fitting regression\n",
-    "models.  "
+    "models."
    ]
   },
   {
@@ -138,19 +134,18 @@
    "id": "5d9e7a14",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:42.845711Z",
-     "iopub.status.busy": "2023-07-26T05:17:42.845521Z",
-     "iopub.status.idle": "2023-07-26T05:17:42.856897Z",
-     "shell.execute_reply": "2023-07-26T05:17:42.856518Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:07.738707Z",
+     "iopub.status.busy": "2023-07-26T19:30:07.738605Z",
+     "iopub.status.idle": "2023-07-26T19:30:07.748329Z",
+     "shell.execute_reply": "2023-07-26T19:30:07.748075Z"
+    }
    },
    "outputs": [],
    "source": [
     "model = MS(Carseats.columns.drop('Sales'), intercept=False)\n",
     "D = model.fit_transform(Carseats)\n",
     "feature_names = list(D.columns)\n",
-    "X = np.asarray(D)\n"
+    "X = np.asarray(D)"
    ]
   },
   {
@@ -173,18 +168,17 @@
    "id": "970c4515",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:42.859418Z",
-     "iopub.status.busy": "2023-07-26T05:17:42.859055Z",
-     "iopub.status.idle": "2023-07-26T05:17:42.866816Z",
-     "shell.execute_reply": "2023-07-26T05:17:42.866395Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:07.749816Z",
+     "iopub.status.busy": "2023-07-26T19:30:07.749731Z",
+     "iopub.status.idle": "2023-07-26T19:30:07.755256Z",
+     "shell.execute_reply": "2023-07-26T19:30:07.754986Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)"
@@ -199,7 +193,7 @@
     "clf = DTC(criterion='entropy',\n",
     "          max_depth=3,\n",
     "          random_state=0)        \n",
-    "clf.fit(X, High)\n"
+    "clf.fit(X, High)"
    ]
   },
   {
@@ -225,12 +219,11 @@
    "id": "2d9a51dc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:42.868805Z",
-     "iopub.status.busy": "2023-07-26T05:17:42.868636Z",
-     "iopub.status.idle": "2023-07-26T05:17:42.872257Z",
-     "shell.execute_reply": "2023-07-26T05:17:42.871848Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:07.756809Z",
+     "iopub.status.busy": "2023-07-26T19:30:07.756708Z",
+     "iopub.status.idle": "2023-07-26T19:30:07.759541Z",
+     "shell.execute_reply": "2023-07-26T19:30:07.759278Z"
+    }
    },
    "outputs": [
     {
@@ -245,7 +238,7 @@
     }
    ],
    "source": [
-    "accuracy_score(High, clf.predict(X))\n"
+    "accuracy_score(High, clf.predict(X))"
    ]
   },
   {
@@ -272,10 +265,10 @@
    "id": "cbc04b67",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:42.874181Z",
-     "iopub.status.busy": "2023-07-26T05:17:42.874073Z",
-     "iopub.status.idle": "2023-07-26T05:17:42.878254Z",
-     "shell.execute_reply": "2023-07-26T05:17:42.877678Z"
+     "iopub.execute_input": "2023-07-26T19:30:07.760932Z",
+     "iopub.status.busy": "2023-07-26T19:30:07.760834Z",
+     "iopub.status.idle": "2023-07-26T19:30:07.763865Z",
+     "shell.execute_reply": "2023-07-26T19:30:07.763631Z"
     }
    },
    "outputs": [
@@ -292,7 +285,7 @@
    ],
    "source": [
     "resid_dev = np.sum(log_loss(High, clf.predict_proba(X)))\n",
-    "resid_dev\n"
+    "resid_dev"
    ]
   },
   {
@@ -315,12 +308,11 @@
    "id": "809830c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:42.880423Z",
-     "iopub.status.busy": "2023-07-26T05:17:42.880256Z",
-     "iopub.status.idle": "2023-07-26T05:17:43.233297Z",
-     "shell.execute_reply": "2023-07-26T05:17:43.232754Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:07.765342Z",
+     "iopub.status.busy": "2023-07-26T19:30:07.765257Z",
+     "iopub.status.idle": "2023-07-26T19:30:08.091241Z",
+     "shell.execute_reply": "2023-07-26T19:30:08.090308Z"
+    }
    },
    "outputs": [
     {
@@ -338,7 +330,7 @@
     "ax = subplots(figsize=(12,12))[1]\n",
     "plot_tree(clf,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);\n"
+    "          ax=ax);"
    ]
   },
   {
@@ -363,10 +355,10 @@
    "id": "abe8c7fc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:43.235787Z",
-     "iopub.status.busy": "2023-07-26T05:17:43.235629Z",
-     "iopub.status.idle": "2023-07-26T05:17:43.238645Z",
-     "shell.execute_reply": "2023-07-26T05:17:43.238197Z"
+     "iopub.execute_input": "2023-07-26T19:30:08.097796Z",
+     "iopub.status.busy": "2023-07-26T19:30:08.094873Z",
+     "iopub.status.idle": "2023-07-26T19:30:08.105509Z",
+     "shell.execute_reply": "2023-07-26T19:30:08.103339Z"
     }
    },
    "outputs": [
@@ -403,7 +395,7 @@
    "source": [
     "print(export_text(clf,\n",
     "                  feature_names=feature_names,\n",
-    "                  show_weights=True))\n"
+    "                  show_weights=True))"
    ]
   },
   {
@@ -428,12 +420,11 @@
    "id": "f7a1f736",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:43.241246Z",
-     "iopub.status.busy": "2023-07-26T05:17:43.241128Z",
-     "iopub.status.idle": "2023-07-26T05:17:43.248339Z",
-     "shell.execute_reply": "2023-07-26T05:17:43.247882Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:08.109547Z",
+     "iopub.status.busy": "2023-07-26T19:30:08.109034Z",
+     "iopub.status.idle": "2023-07-26T19:30:08.130343Z",
+     "shell.execute_reply": "2023-07-26T19:30:08.122380Z"
+    }
    },
    "outputs": [
     {
@@ -455,15 +446,7 @@
     "                             D,\n",
     "                             High,\n",
     "                             cv=validation)\n",
-    "results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "257f1382",
-   "metadata": {},
-   "source": [
-    "    "
+    "results['test_score']"
    ]
   },
   {
@@ -484,12 +467,11 @@
    "id": "c663a623",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:43.250854Z",
-     "iopub.status.busy": "2023-07-26T05:17:43.250675Z",
-     "iopub.status.idle": "2023-07-26T05:17:43.253625Z",
-     "shell.execute_reply": "2023-07-26T05:17:43.253117Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:08.134846Z",
+     "iopub.status.busy": "2023-07-26T19:30:08.134383Z",
+     "iopub.status.idle": "2023-07-26T19:30:08.139441Z",
+     "shell.execute_reply": "2023-07-26T19:30:08.138407Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -508,7 +490,7 @@
    "id": "9e618049",
    "metadata": {},
    "source": [
-    "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation.\n"
+    "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation."
    ]
   },
   {
@@ -517,12 +499,11 @@
    "id": "c2f2a403",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:43.256072Z",
-     "iopub.status.busy": "2023-07-26T05:17:43.255891Z",
-     "iopub.status.idle": "2023-07-26T05:17:43.261392Z",
-     "shell.execute_reply": "2023-07-26T05:17:43.260822Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:08.142863Z",
+     "iopub.status.busy": "2023-07-26T19:30:08.142428Z",
+     "iopub.status.idle": "2023-07-26T19:30:08.151177Z",
+     "shell.execute_reply": "2023-07-26T19:30:08.150610Z"
+    }
    },
    "outputs": [
     {
@@ -539,7 +520,7 @@
    "source": [
     "clf = DTC(criterion='entropy', random_state=0)\n",
     "clf.fit(X_train, High_train)\n",
-    "accuracy_score(High_test, clf.predict(X_test))\n"
+    "accuracy_score(High_test, clf.predict(X_test))"
    ]
   },
   {
@@ -548,7 +529,7 @@
    "metadata": {},
    "source": [
     "Next we use the `cost_complexity_pruning_path()` method of\n",
-    "`clf` to extract cost-complexity values. "
+    "`clf` to extract cost-complexity values."
    ]
   },
   {
@@ -557,19 +538,18 @@
    "id": "b2505658",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:43.264289Z",
-     "iopub.status.busy": "2023-07-26T05:17:43.264075Z",
-     "iopub.status.idle": "2023-07-26T05:17:43.268404Z",
-     "shell.execute_reply": "2023-07-26T05:17:43.267912Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:08.154527Z",
+     "iopub.status.busy": "2023-07-26T19:30:08.154214Z",
+     "iopub.status.idle": "2023-07-26T19:30:08.162522Z",
+     "shell.execute_reply": "2023-07-26T19:30:08.161407Z"
+    }
    },
    "outputs": [],
    "source": [
     "ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)\n",
     "kfold = skm.KFold(10,\n",
     "                  random_state=1,\n",
-    "                  shuffle=True)\n"
+    "                  shuffle=True)"
    ]
   },
   {
@@ -587,12 +567,11 @@
    "id": "09a7bd33",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:43.270876Z",
-     "iopub.status.busy": "2023-07-26T05:17:43.270706Z",
-     "iopub.status.idle": "2023-07-26T05:17:43.553527Z",
-     "shell.execute_reply": "2023-07-26T05:17:43.553080Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:08.166296Z",
+     "iopub.status.busy": "2023-07-26T19:30:08.166071Z",
+     "iopub.status.idle": "2023-07-26T19:30:08.436619Z",
+     "shell.execute_reply": "2023-07-26T19:30:08.436306Z"
+    }
    },
    "outputs": [
     {
@@ -613,7 +592,7 @@
     "                        cv=kfold,\n",
     "                        scoring='accuracy')\n",
     "grid.fit(X_train, High_train)\n",
-    "grid.best_score_\n"
+    "grid.best_score_"
    ]
   },
   {
@@ -630,12 +609,11 @@
    "id": "a75dea32",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:43.556004Z",
-     "iopub.status.busy": "2023-07-26T05:17:43.555851Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.479893Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.479062Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:08.438243Z",
+     "iopub.status.busy": "2023-07-26T19:30:08.438132Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.201634Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.201311Z"
+    }
    },
    "outputs": [
     {
@@ -654,7 +632,7 @@
     "best_ = grid.best_estimator_\n",
     "plot_tree(best_,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);\n"
+    "          ax=ax);"
    ]
   },
   {
@@ -672,12 +650,11 @@
    "id": "3f1e3d19",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.483391Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.483211Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.486537Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.486111Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:09.203364Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.203236Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.205780Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.205471Z"
+    }
    },
    "outputs": [
     {
@@ -692,7 +669,7 @@
     }
    ],
    "source": [
-    "best_.tree_.n_leaves\n"
+    "best_.tree_.n_leaves"
    ]
   },
   {
@@ -712,12 +689,11 @@
    "id": "30f5c2f9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.489160Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.489048Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.498333Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.497705Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:09.207457Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.207344Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.214183Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.213886Z"
+    }
    },
    "outputs": [
     {
@@ -789,7 +765,7 @@
     "                     best_.predict(X_test)))\n",
     "confusion = confusion_table(best_.predict(X_test),\n",
     "                            High_test)\n",
-    "confusion\n"
+    "confusion"
    ]
   },
   {
@@ -797,9 +773,7 @@
    "id": "adbe5709",
    "metadata": {},
    "source": [
-    "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance.\n",
-    "\n",
-    "  "
+    "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance."
    ]
   },
   {
@@ -818,10 +792,10 @@
    "id": "1ca6bc7a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.500404Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.500198Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.510037Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.509664Z"
+     "iopub.execute_input": "2023-07-26T19:30:09.215731Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.215628Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.223241Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.222984Z"
     }
    },
    "outputs": [],
@@ -830,7 +804,7 @@
     "model = MS(Boston.columns.drop('medv'), intercept=False)\n",
     "D = model.fit_transform(Boston)\n",
     "feature_names = list(D.columns)\n",
-    "X = np.asarray(D)\n"
+    "X = np.asarray(D)"
    ]
   },
   {
@@ -839,7 +813,7 @@
    "metadata": {},
    "source": [
     "First, we split the data into training and test sets, and fit the tree\n",
-    "to the training data. Here we use 30% of the data for the test set.\n"
+    "to the training data. Here we use 30% of the data for the test set."
    ]
   },
   {
@@ -848,10 +822,10 @@
    "id": "15cfb553",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.512140Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.511989Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.515238Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.514701Z"
+     "iopub.execute_input": "2023-07-26T19:30:09.224763Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.224672Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.227079Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.226826Z"
     }
    },
    "outputs": [],
@@ -862,7 +836,7 @@
     " y_test) = skm.train_test_split(X,\n",
     "                                Boston['medv'],\n",
     "                                test_size=0.3,\n",
-    "                                random_state=0)\n"
+    "                                random_state=0)"
    ]
   },
   {
@@ -879,16 +853,16 @@
    "id": "d18820a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.517521Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.517330Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.858666Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.858062Z"
+     "iopub.execute_input": "2023-07-26T19:30:09.228509Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.228432Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.502930Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.502524Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1200x1200 with 1 Axes>"
       ]
@@ -903,7 +877,7 @@
     "ax = subplots(figsize=(12,12))[1]\n",
     "plot_tree(reg,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);\n"
+    "          ax=ax);"
    ]
   },
   {
@@ -933,10 +907,10 @@
    "id": "619f8d94",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.861023Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.860905Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.914455Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.913988Z"
+     "iopub.execute_input": "2023-07-26T19:30:09.510607Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.510320Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.559075Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.558768Z"
     }
    },
    "outputs": [],
@@ -950,7 +924,7 @@
     "                        refit=True,\n",
     "                        cv=kfold,\n",
     "                        scoring='neg_mean_squared_error')\n",
-    "G = grid.fit(X_train, y_train)\n"
+    "G = grid.fit(X_train, y_train)"
    ]
   },
   {
@@ -968,12 +942,11 @@
    "id": "779a14b6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.917435Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.917262Z",
-     "iopub.status.idle": "2023-07-26T05:17:44.920852Z",
-     "shell.execute_reply": "2023-07-26T05:17:44.920436Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:09.560903Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.560807Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.563685Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.563424Z"
+    }
    },
    "outputs": [
     {
@@ -989,7 +962,7 @@
    ],
    "source": [
     "best_ = grid.best_estimator_\n",
-    "np.mean((y_test - best_.predict(X_test))**2)\n"
+    "np.mean((y_test - best_.predict(X_test))**2)"
    ]
   },
   {
@@ -1014,17 +987,16 @@
    "id": "2b04030b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:44.923340Z",
-     "iopub.status.busy": "2023-07-26T05:17:44.923189Z",
-     "iopub.status.idle": "2023-07-26T05:17:45.244796Z",
-     "shell.execute_reply": "2023-07-26T05:17:45.244381Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:09.565313Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.565190Z",
+     "iopub.status.idle": "2023-07-26T19:30:09.845669Z",
+     "shell.execute_reply": "2023-07-26T19:30:09.845356Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x1200 with 1 Axes>"
       ]
@@ -1037,16 +1009,7 @@
     "ax = subplots(figsize=(12,12))[1]\n",
     "plot_tree(G.best_estimator_,\n",
     "          feature_names=feature_names,\n",
-    "          ax=ax);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45f99d7e",
-   "metadata": {},
-   "source": [
-    " \n",
-    " "
+    "          ax=ax);"
    ]
   },
   {
@@ -1075,18 +1038,17 @@
    "id": "7dedce31",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:45.248119Z",
-     "iopub.status.busy": "2023-07-26T05:17:45.247930Z",
-     "iopub.status.idle": "2023-07-26T05:17:45.416858Z",
-     "shell.execute_reply": "2023-07-26T05:17:45.416407Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:09.847475Z",
+     "iopub.status.busy": "2023-07-26T19:30:09.847356Z",
+     "iopub.status.idle": "2023-07-26T19:30:10.008824Z",
+     "shell.execute_reply": "2023-07-26T19:30:10.008479Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=12, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=12, random_state=0)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=12, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=12, random_state=0)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "RandomForestRegressor(max_features=12, random_state=0)"
@@ -1099,7 +1061,7 @@
    ],
    "source": [
     "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n",
-    "bag_boston.fit(X_train, y_train)\n"
+    "bag_boston.fit(X_train, y_train)"
    ]
   },
   {
@@ -1119,10 +1081,10 @@
    "id": "1f2c7336",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:45.418831Z",
-     "iopub.status.busy": "2023-07-26T05:17:45.418689Z",
-     "iopub.status.idle": "2023-07-26T05:17:45.525786Z",
-     "shell.execute_reply": "2023-07-26T05:17:45.525224Z"
+     "iopub.execute_input": "2023-07-26T19:30:10.010662Z",
+     "iopub.status.busy": "2023-07-26T19:30:10.010525Z",
+     "iopub.status.idle": "2023-07-26T19:30:10.105797Z",
+     "shell.execute_reply": "2023-07-26T19:30:10.105500Z"
     }
    },
    "outputs": [
@@ -1151,7 +1113,7 @@
     "ax = subplots(figsize=(8,8))[1]\n",
     "y_hat_bag = bag_boston.predict(X_test)\n",
     "ax.scatter(y_hat_bag, y_test)\n",
-    "np.mean((y_test - y_hat_bag)**2)\n"
+    "np.mean((y_test - y_hat_bag)**2)"
    ]
   },
   {
@@ -1172,12 +1134,11 @@
    "id": "0bba2439",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:45.528244Z",
-     "iopub.status.busy": "2023-07-26T05:17:45.528120Z",
-     "iopub.status.idle": "2023-07-26T05:17:46.372586Z",
-     "shell.execute_reply": "2023-07-26T05:17:46.372196Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:10.107534Z",
+     "iopub.status.busy": "2023-07-26T19:30:10.107407Z",
+     "iopub.status.idle": "2023-07-26T19:30:10.910541Z",
+     "shell.execute_reply": "2023-07-26T19:30:10.910243Z"
+    }
    },
    "outputs": [
     {
@@ -1221,12 +1182,11 @@
    "id": "90457fbe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:46.375077Z",
-     "iopub.status.busy": "2023-07-26T05:17:46.374893Z",
-     "iopub.status.idle": "2023-07-26T05:17:46.498984Z",
-     "shell.execute_reply": "2023-07-26T05:17:46.498519Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:10.912250Z",
+     "iopub.status.busy": "2023-07-26T19:30:10.912128Z",
+     "iopub.status.idle": "2023-07-26T19:30:11.021774Z",
+     "shell.execute_reply": "2023-07-26T19:30:11.021451Z"
+    }
    },
    "outputs": [
     {
@@ -1244,7 +1204,7 @@
     "RF_boston = RF(max_features=6,\n",
     "               random_state=0).fit(X_train, y_train)\n",
     "y_hat_RF = RF_boston.predict(X_test)\n",
-    "np.mean((y_test - y_hat_RF)**2)\n"
+    "np.mean((y_test - y_hat_RF)**2)"
    ]
   },
   {
@@ -1264,12 +1224,11 @@
    "id": "8aae1857",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:46.501498Z",
-     "iopub.status.busy": "2023-07-26T05:17:46.501332Z",
-     "iopub.status.idle": "2023-07-26T05:17:46.511203Z",
-     "shell.execute_reply": "2023-07-26T05:17:46.510809Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:11.023526Z",
+     "iopub.status.busy": "2023-07-26T19:30:11.023400Z",
+     "iopub.status.idle": "2023-07-26T19:30:11.029781Z",
+     "shell.execute_reply": "2023-07-26T19:30:11.029457Z"
+    }
    },
    "outputs": [
     {
@@ -1388,9 +1347,7 @@
     "\n",
     "The results indicate that across all of the trees considered in the\n",
     "random forest, the wealth level of the community (`lstat`) and the\n",
-    "house size (`rm`) are by far the two most important variables.\n",
-    "\n",
-    "  "
+    "house size (`rm`) are by far the two most important variables."
    ]
   },
   {
@@ -1422,17 +1379,17 @@
    "id": "887b4fa9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:46.513662Z",
-     "iopub.status.busy": "2023-07-26T05:17:46.513485Z",
-     "iopub.status.idle": "2023-07-26T05:17:50.160797Z",
-     "shell.execute_reply": "2023-07-26T05:17:50.160288Z"
+     "iopub.execute_input": "2023-07-26T19:30:11.031499Z",
+     "iopub.status.busy": "2023-07-26T19:30:11.031393Z",
+     "iopub.status.idle": "2023-07-26T19:30:14.692497Z",
+     "shell.execute_reply": "2023-07-26T19:30:14.692170Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
        "                          random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
        "                          random_state=0)</pre></div></div></div></div></div>"
       ],
@@ -1451,7 +1408,7 @@
     "                   learning_rate=0.001,\n",
     "                   max_depth=3,\n",
     "                   random_state=0)\n",
-    "boost_boston.fit(X_train, y_train)\n"
+    "boost_boston.fit(X_train, y_train)"
    ]
   },
   {
@@ -1470,10 +1427,10 @@
    "id": "6c56696c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:50.163556Z",
-     "iopub.status.busy": "2023-07-26T05:17:50.163389Z",
-     "iopub.status.idle": "2023-07-26T05:17:50.751622Z",
-     "shell.execute_reply": "2023-07-26T05:17:50.751113Z"
+     "iopub.execute_input": "2023-07-26T19:30:14.694174Z",
+     "iopub.status.busy": "2023-07-26T19:30:14.694070Z",
+     "iopub.status.idle": "2023-07-26T19:30:15.152622Z",
+     "shell.execute_reply": "2023-07-26T19:30:15.152291Z"
     }
    },
    "outputs": [
@@ -1503,7 +1460,7 @@
     "        test_error,\n",
     "        'r',\n",
     "        label='Test')\n",
-    "ax.legend();\n"
+    "ax.legend();"
    ]
   },
   {
@@ -1520,10 +1477,10 @@
    "id": "8a636f63",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:50.753880Z",
-     "iopub.status.busy": "2023-07-26T05:17:50.753719Z",
-     "iopub.status.idle": "2023-07-26T05:17:50.765303Z",
-     "shell.execute_reply": "2023-07-26T05:17:50.764814Z"
+     "iopub.execute_input": "2023-07-26T19:30:15.154541Z",
+     "iopub.status.busy": "2023-07-26T19:30:15.154425Z",
+     "iopub.status.idle": "2023-07-26T19:30:15.166138Z",
+     "shell.execute_reply": "2023-07-26T19:30:15.165843Z"
     }
    },
    "outputs": [
@@ -1540,7 +1497,7 @@
    ],
    "source": [
     "y_hat_boost = boost_boston.predict(X_test);\n",
-    "np.mean((y_test - y_hat_boost)**2)\n"
+    "np.mean((y_test - y_hat_boost)**2)"
    ]
   },
   {
@@ -1561,12 +1518,11 @@
    "id": "4e2ab3c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:50.767698Z",
-     "iopub.status.busy": "2023-07-26T05:17:50.767534Z",
-     "iopub.status.idle": "2023-07-26T05:17:53.090781Z",
-     "shell.execute_reply": "2023-07-26T05:17:53.090355Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:15.167842Z",
+     "iopub.status.busy": "2023-07-26T19:30:15.167727Z",
+     "iopub.status.idle": "2023-07-26T19:30:17.555235Z",
+     "shell.execute_reply": "2023-07-26T19:30:17.554938Z"
+    }
    },
    "outputs": [
     {
@@ -1588,7 +1544,7 @@
     "boost_boston.fit(X_train,\n",
     "                 y_train)\n",
     "y_hat_boost = boost_boston.predict(X_test);\n",
-    "np.mean((y_test - y_hat_boost)**2)\n"
+    "np.mean((y_test - y_hat_boost)**2)"
    ]
   },
   {
@@ -1597,9 +1553,7 @@
    "metadata": {},
    "source": [
     "In this case, using $\\lambda=0.2$ leads to a almost the same test MSE\n",
-    "as $\\lambda=0.001$.\n",
-    "\n",
-    " "
+    "as $\\lambda=0.001$."
    ]
   },
   {
@@ -1628,18 +1582,17 @@
    "id": "b0b41c04",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:53.093602Z",
-     "iopub.status.busy": "2023-07-26T05:17:53.093377Z",
-     "iopub.status.idle": "2023-07-26T05:17:54.986360Z",
-     "shell.execute_reply": "2023-07-26T05:17:54.985944Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:17.557027Z",
+     "iopub.status.busy": "2023-07-26T19:30:17.556909Z",
+     "iopub.status.idle": "2023-07-26T19:30:18.997116Z",
+     "shell.execute_reply": "2023-07-26T19:30:18.996840Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BART(burnin=5, ndraw=15, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BART</label><div class=\"sk-toggleable__content\"><pre>BART(burnin=5, ndraw=15, random_state=0)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BART(burnin=5, ndraw=15, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BART</label><div class=\"sk-toggleable__content\"><pre>BART(burnin=5, ndraw=15, random_state=0)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "BART(burnin=5, ndraw=15, random_state=0)"
@@ -1652,7 +1605,7 @@
    ],
    "source": [
     "bart_boston = BART(random_state=0, burnin=5, ndraw=15)\n",
-    "bart_boston.fit(X_train, y_train)\n"
+    "bart_boston.fit(X_train, y_train)"
    ]
   },
   {
@@ -1669,12 +1622,11 @@
    "id": "fda8fffc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:54.988616Z",
-     "iopub.status.busy": "2023-07-26T05:17:54.988463Z",
-     "iopub.status.idle": "2023-07-26T05:17:55.919353Z",
-     "shell.execute_reply": "2023-07-26T05:17:55.918371Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:18.998761Z",
+     "iopub.status.busy": "2023-07-26T19:30:18.998658Z",
+     "iopub.status.idle": "2023-07-26T19:30:19.293073Z",
+     "shell.execute_reply": "2023-07-26T19:30:19.292790Z"
+    }
    },
    "outputs": [
     {
@@ -1690,7 +1642,7 @@
    ],
    "source": [
     "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n",
-    "np.mean((y_test - yhat_test)**2)\n"
+    "np.mean((y_test - yhat_test)**2)"
    ]
   },
   {
@@ -1708,12 +1660,11 @@
    "id": "be77f0e4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:55.922114Z",
-     "iopub.status.busy": "2023-07-26T05:17:55.921834Z",
-     "iopub.status.idle": "2023-07-26T05:17:55.926261Z",
-     "shell.execute_reply": "2023-07-26T05:17:55.925458Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:19.294666Z",
+     "iopub.status.busy": "2023-07-26T19:30:19.294560Z",
+     "iopub.status.idle": "2023-07-26T19:30:19.297309Z",
+     "shell.execute_reply": "2023-07-26T19:30:19.297024Z"
+    }
    },
    "outputs": [
     {
@@ -1742,26 +1693,15 @@
    "source": [
     "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n",
     "                               index=D.columns)\n",
-    "var_inclusion\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cf40b6ac",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "  \n",
-    " \n",
-    "\n"
+    "var_inclusion"
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1773,7 +1713,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/Ch9-svm-lab.ipynb b/Ch9-svm-lab.ipynb
index a666df1..368eb05 100644
--- a/Ch9-svm-lab.ipynb
+++ b/Ch9-svm-lab.ipynb
@@ -5,9 +5,7 @@
    "id": "d45c6d2b",
    "metadata": {},
    "source": [
-    "\n",
-    "# Chapter 9\n",
-    "\n"
+    "# Chapter 9"
    ]
   },
   {
@@ -28,19 +26,18 @@
    "id": "eeaa5be0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:57.286672Z",
-     "iopub.status.busy": "2023-07-26T05:17:57.286340Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.454461Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.453915Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:20.613956Z",
+     "iopub.status.busy": "2023-07-26T19:30:20.613862Z",
+     "iopub.status.idle": "2023-07-26T19:30:21.610751Z",
+     "shell.execute_reply": "2023-07-26T19:30:21.610246Z"
+    }
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from matplotlib.pyplot import subplots, cm\n",
     "import sklearn.model_selection as skm\n",
-    "from ISLP import load_data, confusion_table\n"
+    "from ISLP import load_data, confusion_table"
    ]
   },
   {
@@ -58,17 +55,17 @@
    "id": "41a59634",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.457424Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.457095Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.490405Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.490061Z"
+     "iopub.execute_input": "2023-07-26T19:30:21.613062Z",
+     "iopub.status.busy": "2023-07-26T19:30:21.612758Z",
+     "iopub.status.idle": "2023-07-26T19:30:21.648403Z",
+     "shell.execute_reply": "2023-07-26T19:30:21.648111Z"
     }
    },
    "outputs": [],
    "source": [
     "from sklearn.svm import SVC\n",
     "from ISLP.svm import plot as plot_svm\n",
-    "from sklearn.metrics import RocCurveDisplay\n"
+    "from sklearn.metrics import RocCurveDisplay"
    ]
   },
   {
@@ -86,15 +83,15 @@
    "id": "c9a175d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.493151Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.492879Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.496409Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.495164Z"
+     "iopub.execute_input": "2023-07-26T19:30:21.650168Z",
+     "iopub.status.busy": "2023-07-26T19:30:21.650058Z",
+     "iopub.status.idle": "2023-07-26T19:30:21.651930Z",
+     "shell.execute_reply": "2023-07-26T19:30:21.651629Z"
     }
    },
    "outputs": [],
    "source": [
-    "roc_curve = RocCurveDisplay.from_estimator # shorthand\n"
+    "roc_curve = RocCurveDisplay.from_estimator # shorthand"
    ]
   },
   {
@@ -126,12 +123,11 @@
    "id": "a7216b47",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.499025Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.498850Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.613732Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.613136Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:21.653381Z",
+     "iopub.status.busy": "2023-07-26T19:30:21.653281Z",
+     "iopub.status.idle": "2023-07-26T19:30:21.771451Z",
+     "shell.execute_reply": "2023-07-26T19:30:21.769306Z"
+    }
    },
    "outputs": [
     {
@@ -154,7 +150,7 @@
     "ax.scatter(X[:,0],\n",
     "           X[:,1],\n",
     "           c=y,\n",
-    "           cmap=cm.coolwarm);\n"
+    "           cmap=cm.coolwarm);"
    ]
   },
   {
@@ -171,18 +167,17 @@
    "id": "ed329198",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.616777Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.616601Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.621468Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.621124Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:21.776220Z",
+     "iopub.status.busy": "2023-07-26T19:30:21.775574Z",
+     "iopub.status.idle": "2023-07-26T19:30:21.791582Z",
+     "shell.execute_reply": "2023-07-26T19:30:21.790533Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "SVC(C=10, kernel='linear')"
@@ -195,7 +190,7 @@
    ],
    "source": [
     "svm_linear = SVC(C=10, kernel='linear')\n",
-    "svm_linear.fit(X, y)\n"
+    "svm_linear.fit(X, y)"
    ]
   },
   {
@@ -215,10 +210,10 @@
    "id": "95494b8b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.623538Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.623369Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.810058Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.809447Z"
+     "iopub.execute_input": "2023-07-26T19:30:21.798277Z",
+     "iopub.status.busy": "2023-07-26T19:30:21.797873Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.041587Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.041253Z"
     }
    },
    "outputs": [
@@ -238,7 +233,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_linear,\n",
-    "         ax=ax)\n"
+    "         ax=ax)"
    ]
   },
   {
@@ -260,12 +255,11 @@
    "id": "98c2236f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.812870Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.812658Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.967044Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.966462Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:22.043302Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.043193Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.185489Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.185015Z"
+    }
    },
    "outputs": [
     {
@@ -286,7 +280,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_linear_small,\n",
-    "         ax=ax)\n"
+    "         ax=ax)"
    ]
   },
   {
@@ -306,12 +300,11 @@
    "id": "b498f594",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.969920Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.969735Z",
-     "iopub.status.idle": "2023-07-26T05:17:58.973409Z",
-     "shell.execute_reply": "2023-07-26T05:17:58.972682Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:22.187546Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.187386Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.190140Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.189804Z"
+    }
    },
    "outputs": [
     {
@@ -326,7 +319,7 @@
     }
    ],
    "source": [
-    "svm_linear.coef_\n"
+    "svm_linear.coef_"
    ]
   },
   {
@@ -344,12 +337,11 @@
    "id": "b65e80d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:58.975865Z",
-     "iopub.status.busy": "2023-07-26T05:17:58.975747Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.006899Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.006545Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:22.192137Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.191917Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.221281Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.220919Z"
+    }
    },
    "outputs": [
     {
@@ -373,7 +365,7 @@
     "                        cv=kfold,\n",
     "                        scoring='accuracy')\n",
     "grid.fit(X, y)\n",
-    "grid.best_params_\n"
+    "grid.best_params_"
    ]
   },
   {
@@ -392,12 +384,11 @@
    "id": "bba8fad7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.010681Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.010231Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.014165Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.013759Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:22.223145Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.222994Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.225490Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.225195Z"
+    }
    },
    "outputs": [
     {
@@ -412,7 +403,7 @@
     }
    ],
    "source": [
-    "grid.cv_results_[('mean_test_score')]\n"
+    "grid.cv_results_[('mean_test_score')]"
    ]
   },
   {
@@ -433,17 +424,17 @@
    "id": "ad64269d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.017638Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.017453Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.020397Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.019642Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.227113Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.227004Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.229099Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.228833Z"
     }
    },
    "outputs": [],
    "source": [
     "X_test = rng.standard_normal((20, 2))\n",
     "y_test = np.array([-1]*10+[1]*10)\n",
-    "X_test[y_test==1] += 1\n"
+    "X_test[y_test==1] += 1"
    ]
   },
   {
@@ -462,10 +453,10 @@
    "id": "5107fca1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.026576Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.026365Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.033664Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.033173Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.230650Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.230546Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.236572Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.236281Z"
     }
    },
    "outputs": [
@@ -529,7 +520,7 @@
    "source": [
     "best_ = grid.best_estimator_\n",
     "y_test_hat = best_.predict(X_test)\n",
-    "confusion_table(y_test_hat, y_test)\n"
+    "confusion_table(y_test_hat, y_test)"
    ]
   },
   {
@@ -549,10 +540,10 @@
    "id": "0320d9e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.035936Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.035645Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.041475Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.041054Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.238263Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.238141Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.242738Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.242453Z"
     }
    },
    "outputs": [
@@ -617,7 +608,7 @@
     "svm_ = SVC(C=0.001,\n",
     "           kernel='linear').fit(X, y)\n",
     "y_test_hat = svm_.predict(X_test)\n",
-    "confusion_table(y_test_hat, y_test)\n"
+    "confusion_table(y_test_hat, y_test)"
    ]
   },
   {
@@ -640,10 +631,10 @@
    "id": "84d7e778",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.043762Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.043557Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.144334Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.143881Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.244328Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.244224Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.371782Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.371427Z"
     }
    },
    "outputs": [
@@ -661,7 +652,7 @@
    "source": [
     "X[y==1] += 1.9;\n",
     "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);\n"
+    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);"
    ]
   },
   {
@@ -678,10 +669,10 @@
    "id": "abb1f8be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.147029Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.146868Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.154787Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.153508Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.380382Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.380213Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.385861Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.385389Z"
     }
    },
    "outputs": [
@@ -745,7 +736,7 @@
    "source": [
     "svm_ = SVC(C=1e5, kernel='linear').fit(X, y)\n",
     "y_hat = svm_.predict(X)\n",
-    "confusion_table(y_hat, y)\n"
+    "confusion_table(y_hat, y)"
    ]
   },
   {
@@ -756,7 +747,7 @@
     "We fit the\n",
     "support vector classifier and plot the resulting hyperplane, using a\n",
     "very large value of `C` so that no observations are\n",
-    "misclassified. "
+    "misclassified."
    ]
   },
   {
@@ -765,12 +756,11 @@
    "id": "2e4ed2f5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.157293Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.157100Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.287805Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.287264Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:22.387842Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.387704Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.507545Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.507157Z"
+    }
    },
    "outputs": [
     {
@@ -789,7 +779,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_,\n",
-    "         ax=ax)\n"
+    "         ax=ax)"
    ]
   },
   {
@@ -810,10 +800,10 @@
    "id": "164a611c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.291112Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.290933Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.296989Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.296618Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.509336Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.509220Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.514501Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.513847Z"
     }
    },
    "outputs": [
@@ -877,7 +867,7 @@
    "source": [
     "svm_ = SVC(C=0.1, kernel='linear').fit(X, y)\n",
     "y_hat = svm_.predict(X)\n",
-    "confusion_table(y_hat, y)\n"
+    "confusion_table(y_hat, y)"
    ]
   },
   {
@@ -897,12 +887,11 @@
    "id": "c67591a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.299723Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.299515Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.442236Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.441618Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:22.516223Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.516110Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.643367Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.643066Z"
+    }
    },
    "outputs": [
     {
@@ -921,7 +910,7 @@
     "plot_svm(X,\n",
     "         y,\n",
     "         svm_,\n",
-    "         ax=ax)\n"
+    "         ax=ax)"
    ]
   },
   {
@@ -950,10 +939,10 @@
    "id": "322be574",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.445260Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.445043Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.448424Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.447969Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.645070Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.644945Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.647237Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.646934Z"
     }
    },
    "outputs": [],
@@ -961,7 +950,7 @@
     "X = rng.standard_normal((200, 2))\n",
     "X[:100] += 2\n",
     "X[100:150] -= 2\n",
-    "y = np.array([1]*150+[2]*50)\n"
+    "y = np.array([1]*150+[2]*50)"
    ]
   },
   {
@@ -978,18 +967,17 @@
    "id": "04fda182",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.451843Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.451105Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.552259Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.551787Z"
-    },
-    "lines_to_next_cell": 2
+     "iopub.execute_input": "2023-07-26T19:30:22.648795Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.648693Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.737682Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.737352Z"
+    }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x17641f9d0>"
+       "<matplotlib.collections.PathCollection at 0x157b6e810>"
       ]
      },
      "execution_count": 20,
@@ -1012,7 +1000,7 @@
     "ax.scatter(X[:,0],\n",
     "           X[:,1],\n",
     "           c=y,\n",
-    "           cmap=cm.coolwarm)\n"
+    "           cmap=cm.coolwarm)"
    ]
   },
   {
@@ -1031,17 +1019,17 @@
    "id": "0c2690d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.554419Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.554263Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.559300Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.558839Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.739293Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.739179Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.743290Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.743048Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, gamma=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=1)</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, gamma=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=1)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "SVC(C=1, gamma=1)"
@@ -1061,7 +1049,7 @@
     "                                test_size=0.5,\n",
     "                                random_state=0)\n",
     "svm_rbf = SVC(kernel=\"rbf\", gamma=1, C=1)\n",
-    "svm_rbf.fit(X_train, y_train)\n"
+    "svm_rbf.fit(X_train, y_train)"
    ]
   },
   {
@@ -1070,7 +1058,7 @@
    "metadata": {},
    "source": [
     "The plot shows that the resulting SVM has a decidedly non-linear\n",
-    "boundary. "
+    "boundary."
    ]
   },
   {
@@ -1079,10 +1067,10 @@
    "id": "3eb171e8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.561548Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.561354Z",
-     "iopub.status.idle": "2023-07-26T05:17:59.836831Z",
-     "shell.execute_reply": "2023-07-26T05:17:59.836408Z"
+     "iopub.execute_input": "2023-07-26T19:30:22.744866Z",
+     "iopub.status.busy": "2023-07-26T19:30:22.744774Z",
+     "iopub.status.idle": "2023-07-26T19:30:22.999527Z",
+     "shell.execute_reply": "2023-07-26T19:30:22.999240Z"
     }
    },
    "outputs": [
@@ -1102,7 +1090,7 @@
     "plot_svm(X_train,\n",
     "         y_train,\n",
     "         svm_rbf,\n",
-    "         ax=ax)\n"
+    "         ax=ax)"
    ]
   },
   {
@@ -1123,10 +1111,10 @@
    "id": "9a6b905b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:17:59.839191Z",
-     "iopub.status.busy": "2023-07-26T05:17:59.838970Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.004302Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.003731Z"
+     "iopub.execute_input": "2023-07-26T19:30:23.001234Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.001125Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.153325Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.152993Z"
     }
    },
    "outputs": [
@@ -1148,7 +1136,7 @@
     "plot_svm(X_train,\n",
     "         y_train,\n",
     "         svm_rbf,\n",
-    "         ax=ax)\n"
+    "         ax=ax)"
    ]
   },
   {
@@ -1167,10 +1155,10 @@
    "id": "5ab01d6c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.007020Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.006845Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.113455Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.112946Z"
+     "iopub.execute_input": "2023-07-26T19:30:23.155062Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.154942Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.246256Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.245948Z"
     }
    },
    "outputs": [
@@ -1196,7 +1184,7 @@
     "                        cv=kfold,\n",
     "                        scoring='accuracy');\n",
     "grid.fit(X_train, y_train)\n",
-    "grid.best_params_\n"
+    "grid.best_params_"
    ]
   },
   {
@@ -1215,10 +1203,10 @@
    "id": "166a6acb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.116122Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.115942Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.360989Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.360361Z"
+     "iopub.execute_input": "2023-07-26T19:30:23.247818Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.247730Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.480058Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.479735Z"
     }
    },
    "outputs": [
@@ -1298,7 +1286,7 @@
     "         ax=ax)\n",
     "\n",
     "y_hat_test = best_svm.predict(X_test)\n",
-    "confusion_table(y_hat_test, y_test)\n"
+    "confusion_table(y_hat_test, y_test)"
    ]
   },
   {
@@ -1349,12 +1337,11 @@
    "id": "0607fc41",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.363471Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.363318Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.469265Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.468690Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:23.481797Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.481689Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.570996Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.570713Z"
+    }
    },
    "outputs": [
     {
@@ -1375,7 +1362,7 @@
     "          y_train,\n",
     "          name='Training',\n",
     "          color='r',\n",
-    "          ax=ax);\n"
+    "          ax=ax);"
    ]
   },
   {
@@ -1394,10 +1381,10 @@
    "id": "5211a882",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.471742Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.471575Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.629647Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.629119Z"
+     "iopub.execute_input": "2023-07-26T19:30:23.572826Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.572704Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.710067Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.709718Z"
     }
    },
    "outputs": [
@@ -1423,7 +1410,7 @@
     "          y_train,\n",
     "          name='Training $\\gamma=50$',\n",
     "          color='r',\n",
-    "          ax=ax);\n"
+    "          ax=ax);"
    ]
   },
   {
@@ -1443,10 +1430,10 @@
    "id": "12acc4ff",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.632175Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.631994Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.636710Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.636238Z"
+     "iopub.execute_input": "2023-07-26T19:30:23.711836Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.711704Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.715844Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.715547Z"
     }
    },
    "outputs": [],
@@ -1457,7 +1444,7 @@
     "          name='Test $\\gamma=50$',\n",
     "          color='b',\n",
     "          ax=ax)\n",
-    "fig;\n"
+    "fig;"
    ]
   },
   {
@@ -1474,10 +1461,10 @@
    "id": "21c81913",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.639004Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.638813Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.753454Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.752986Z"
+     "iopub.execute_input": "2023-07-26T19:30:23.717442Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.717349Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.813301Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.812976Z"
     }
    },
    "outputs": [
@@ -1505,7 +1492,7 @@
     "              y_,\n",
     "              name=name,\n",
     "              ax=ax,\n",
-    "              color=c)\n"
+    "              color=c)"
    ]
   },
   {
@@ -1529,10 +1516,10 @@
    "id": "2fff4fa8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.756116Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.755939Z",
-     "iopub.status.idle": "2023-07-26T05:18:00.855998Z",
-     "shell.execute_reply": "2023-07-26T05:18:00.855206Z"
+     "iopub.execute_input": "2023-07-26T19:30:23.815030Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.814911Z",
+     "iopub.status.idle": "2023-07-26T19:30:23.901789Z",
+     "shell.execute_reply": "2023-07-26T19:30:23.901472Z"
     }
    },
    "outputs": [
@@ -1553,7 +1540,7 @@
     "y = np.hstack([y, [0]*50])\n",
     "X[y==0,1] += 2\n",
     "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);\n"
+    "ax.scatter(X[:,0], X[:,1], c=y, cmap=cm.coolwarm);"
    ]
   },
   {
@@ -1570,12 +1557,11 @@
    "id": "5396f2df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:00.861579Z",
-     "iopub.status.busy": "2023-07-26T05:18:00.861401Z",
-     "iopub.status.idle": "2023-07-26T05:18:01.455936Z",
-     "shell.execute_reply": "2023-07-26T05:18:01.455227Z"
-    },
-    "lines_to_next_cell": 0
+     "iopub.execute_input": "2023-07-26T19:30:23.903488Z",
+     "iopub.status.busy": "2023-07-26T19:30:23.903367Z",
+     "iopub.status.idle": "2023-07-26T19:30:24.478035Z",
+     "shell.execute_reply": "2023-07-26T19:30:24.477752Z"
+    }
    },
    "outputs": [
     {
@@ -1600,7 +1586,7 @@
     "         y,\n",
     "         svm_rbf_3,\n",
     "         scatter_cmap=cm.tab10,\n",
-    "         ax=ax)\n"
+    "         ax=ax)"
    ]
   },
   {
@@ -1634,10 +1620,10 @@
    "id": "f63c575e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:01.458214Z",
-     "iopub.status.busy": "2023-07-26T05:18:01.458015Z",
-     "iopub.status.idle": "2023-07-26T05:18:01.539063Z",
-     "shell.execute_reply": "2023-07-26T05:18:01.538275Z"
+     "iopub.execute_input": "2023-07-26T19:30:24.479854Z",
+     "iopub.status.busy": "2023-07-26T19:30:24.479740Z",
+     "iopub.status.idle": "2023-07-26T19:30:24.553046Z",
+     "shell.execute_reply": "2023-07-26T19:30:24.552768Z"
     }
    },
    "outputs": [
@@ -1654,7 +1640,7 @@
    ],
    "source": [
     "Khan = load_data('Khan')\n",
-    "Khan['xtrain'].shape, Khan['xtest'].shape\n"
+    "Khan['xtrain'].shape, Khan['xtest'].shape"
    ]
   },
   {
@@ -1671,7 +1657,7 @@
     "large number of features relative to the number of observations. This\n",
     "suggests that we should use a linear kernel, because the additional\n",
     "flexibility that will result from using a polynomial or radial kernel \n",
-    "is unnecessary. "
+    "is unnecessary."
    ]
   },
   {
@@ -1680,10 +1666,10 @@
    "id": "32091338",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:01.541953Z",
-     "iopub.status.busy": "2023-07-26T05:18:01.541758Z",
-     "iopub.status.idle": "2023-07-26T05:18:01.578259Z",
-     "shell.execute_reply": "2023-07-26T05:18:01.577863Z"
+     "iopub.execute_input": "2023-07-26T19:30:24.554699Z",
+     "iopub.status.busy": "2023-07-26T19:30:24.554597Z",
+     "iopub.status.idle": "2023-07-26T19:30:24.583168Z",
+     "shell.execute_reply": "2023-07-26T19:30:24.582899Z"
     }
    },
    "outputs": [
@@ -1772,7 +1758,7 @@
     "khan_linear = SVC(kernel='linear', C=10)\n",
     "khan_linear.fit(Khan['xtrain'], Khan['ytrain'])\n",
     "confusion_table(khan_linear.predict(Khan['xtrain']),\n",
-    "                Khan['ytrain'])\n"
+    "                Khan['ytrain'])"
    ]
   },
   {
@@ -1794,10 +1780,10 @@
    "id": "d9058023",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:18:01.582842Z",
-     "iopub.status.busy": "2023-07-26T05:18:01.582631Z",
-     "iopub.status.idle": "2023-07-26T05:18:01.598298Z",
-     "shell.execute_reply": "2023-07-26T05:18:01.597510Z"
+     "iopub.execute_input": "2023-07-26T19:30:24.584794Z",
+     "iopub.status.busy": "2023-07-26T19:30:24.584676Z",
+     "iopub.status.idle": "2023-07-26T19:30:24.595404Z",
+     "shell.execute_reply": "2023-07-26T19:30:24.595053Z"
     }
    },
    "outputs": [
@@ -1884,7 +1870,7 @@
    ],
    "source": [
     "confusion_table(khan_linear.predict(Khan['xtest']),\n",
-    "                Khan['ytest'])\n"
+    "                Khan['ytest'])"
    ]
   },
   {
@@ -1892,16 +1878,15 @@
    "id": "d0d5aba4",
    "metadata": {},
    "source": [
-    "We see that using `C=10` yields two test set errors on these data.\n",
-    "\n"
+    "We see that using `C=10` yields two test set errors on these data."
    ]
   }
  ],
  "metadata": {
   "jupytext": {
    "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+   "formats": "ipynb,md:myst",
+   "main_language": "python"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1913,7 +1898,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/frozen_requirements.txt b/frozen_requirements.txt
index 27d4e1a..4bc29f2 100644
--- a/frozen_requirements.txt
+++ b/frozen_requirements.txt
@@ -1,11 +1,11 @@
-numpy==1.21.6
-scipy==1.10.1
-pandas==1.4.1
-lxml==4.8.0
-scikit-learn==1.2.2
-joblib==1.2.0
-statsmodels==0.13.2
-lifelines==0.27.6
-pygam==0.8.0
+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
 
diff --git a/torch_requirements.txt b/torch_requirements.txt
index 374ee22..c6ad504 100644
--- a/torch_requirements.txt
+++ b/torch_requirements.txt
@@ -1,5 +1,5 @@
-torch==2.0.0
-torchvision==0.15.1
-pytorch_lightning==1.7.7
-torchinfo==1.7.2
-torchmetrics==0.9.3
+torch==2.0.1
+torchvision==0.15.2
+pytorch-lightning==2.0.6
+torchinfo==1.8.0
+torchmetrics==1.0.1